Relevant bibliographies by topics / Visual object recognition

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Visual object recognition'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2023

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Journal articles on the topic "Visual object recognition"

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Logothetis, N. K., and D. L. Sheinberg. "Visual Object Recognition." Annual Review of Neuroscience 19, no. 1 (March 1996): 577–621. http://dx.doi.org/10.1146/annurev.ne.19.030196.003045.

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Grauman, Kristen, and Bastian Leibe. "Visual Object Recognition." Synthesis Lectures on Artificial Intelligence and Machine Learning 5, no. 2 (April 19, 2011): 1–181. http://dx.doi.org/10.2200/s00332ed1v01y201103aim011.

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Grill-Spector, Kalanit, and Nancy Kanwisher. "Visual Recognition." Psychological Science 16, no. 2 (February 2005): 152–60. http://dx.doi.org/10.1111/j.0956-7976.2005.00796.x.

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What is the sequence of processing steps involved in visual object recognition? We varied the exposure duration of natural images and measured subjects' performance on three different tasks, each designed to tap a different candidate component process of object recognition. For each exposure duration, accuracy was lower and reaction time longer on a within-category identification task (e.g., distinguishing pigeons from other birds) than on a perceptual categorization task (e.g., birds vs. cars). However, strikingly, at each exposure duration, subjects performed just as quickly and accurately on the categorization task as they did on a task requiring only object detection: By the time subjects knew an image contained an object at all, they already knew its category. These findings place powerful constraints on theories of object recognition.
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Vasylenko, Mykola, and Maksym Haida. "Visual Object Recognition System." Electronics and Control Systems 3, no. 73 (November 24, 2022): 9–19. http://dx.doi.org/10.18372/1990-5548.73.17007.

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This article introduces the problem of object detection and recognition. The potential mobility of this solution, ease of installation and ease of initial setup, as well as the absence of expensive, resource-intensive and complex image collection and processing systems are presented. Solutions to the problem are demonstrated, along with the advantages and disadvantages of each. The selection of contours by a filter based on the Prewitt operator and a detector of characteristic points is an algorithm of the system, developed within the framework of object recognition techniques. The reader can follow the interim and final demonstrations of the system algorithm in this article to learn about its advantages over traditional video surveillance systems, as well as some of its disadvantages. A webcam with a video frame rate of 25 frames per second, a mobile phone and a PC with the Matlab2020 programming environment installed (due to its convenience and built-in image processing functions) are required to illustrate how the system works.
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Jiao, Chenlei, Binbin Lian, Zhe Wang, Yimin Song, and Tao Sun. "Visual–tactile object recognition of a soft gripper based on faster Region-based Convolutional Neural Network and machining learning algorithm." International Journal of Advanced Robotic Systems 17, no. 5 (September 1, 2020): 172988142094872. http://dx.doi.org/10.1177/1729881420948727.

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Object recognition is a prerequisite to control a soft gripper successfully grasping an unknown object. Visual and tactile recognitions are two commonly used methods in a grasping system. Visual recognition is limited if the size and weight of the objects are involved, whereas the efficiency of tactile recognition is a problem. A visual–tactile recognition method is proposed to overcome the disadvantages of both methods in this article. The design and fabrication of the soft gripper considering the visual and tactile sensors are implemented, where the Kinect v2 is adopted for visual information, bending and pressure sensors are embedded to the soft fingers for tactile information. The proposed method is divided into three steps: initial recognition by vision, detail recognition by touch, and a data fusion decision making. Experiments show that the visual–tactile recognition has the best results. The average recognition accuracy of the daily objects by the proposed method is also the highest. The feasibility of the visual–tactile recognition is verified.
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Tannenbaum, Allen, Anthony Yezzi, and Alex Goldstein. "Visual Tracking and Object Recognition." IFAC Proceedings Volumes 34, no. 6 (July 2001): 1539–42. http://dx.doi.org/10.1016/s1474-6670(17)35408-3.

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Guo, Fei, Yuan Yang, and Yong Gao. "Optimization of Visual Information Presentation for Visual Prosthesis." International Journal of Biomedical Imaging 2018 (2018): 1–12. http://dx.doi.org/10.1155/2018/3198342.

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Visual prosthesis applying electrical stimulation to restore visual function for the blind has promising prospects. However, due to the low resolution, limited visual field, and the low dynamic range of the visual perception, huge loss of information occurred when presenting daily scenes. The ability of object recognition in real-life scenarios is severely restricted for prosthetic users. To overcome the limitations, optimizing the visual information in the simulated prosthetic vision has been the focus of research. This paper proposes two image processing strategies based on a salient object detection technique. The two processing strategies enable the prosthetic implants to focus on the object of interest and suppress the background clutter. Psychophysical experiments show that techniques such as foreground zooming with background clutter removal and foreground edge detection with background reduction have positive impacts on the task of object recognition in simulated prosthetic vision. By using edge detection and zooming technique, the two processing strategies significantly improve the recognition accuracy of objects. We can conclude that the visual prosthesis using our proposed strategy can assist the blind to improve their ability to recognize objects. The results will provide effective solutions for the further development of visual prosthesis.
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Rennig, Johannes, Sonja Cornelsen, Helmut Wilhelm, Marc Himmelbach, and Hans-Otto Karnath. "Preserved Expert Object Recognition in a Case of Visual Hemiagnosia." Journal of Cognitive Neuroscience 30, no. 2 (February 2018): 131–43. http://dx.doi.org/10.1162/jocn_a_01193.

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We examined a stroke patient (HWS) with a unilateral lesion of the right medial ventral visual stream, involving the right fusiform and parahippocampal gyri. In a number of object recognition tests with lateralized presentations of target stimuli, HWS showed significant symptoms of hemiagnosia with contralesional recognition deficits for everyday objects. We further explored the patient's capacities of visual expertise that were acquired before the current perceptual impairment became effective. We confronted him with objects he was an expert for already before stroke onset and compared this performance with the recognition of familiar everyday objects. HWS was able to identify significantly more of the specific (“expert”) than of the everyday objects on the affected contralesional side. This observation of better expert object recognition in visual hemiagnosia allows for several interpretations. The results may be caused by enhanced information processing for expert objects in the ventral system in the affected or the intact hemisphere. Expert knowledge could trigger top–down mechanisms supporting object recognition despite of impaired basic functions of object processing. More importantly, the current work demonstrates that top–down mechanisms of visual expertise influence object recognition at an early stage, probably before visual object information propagates to modules of higher object recognition. Because HWS showed a lesion to the fusiform gyrus and spared capacities of expert object recognition, the current study emphasizes possible contributions of areas outside the ventral stream to visual expertise.
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Woods, Andrew T., Allison Moore, and Fiona N. Newell. "Canonical Views in Haptic Object Perception." Perception 37, no. 12 (January 1, 2008): 1867–78. http://dx.doi.org/10.1068/p6038.

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Previous investigations of visual object recognition have found that some views of both familiar and unfamiliar objects promote more efficient recognition performance than other views. These views are considered as canonical and are often the views that present the most information about an object's 3-D structure and features in the image. Although objects can also be efficiently recognised with touch alone, little is known whether some views promote more efficient recognition than others. This may seem unlikely, given that the object structure and features are readily available to the hand during object exploration. We conducted two experiments to investigate whether canonical views existed in haptic object recognition. In the first, participants were required to position each object in a way that would present the best view for learning the object with touch alone. We found a large degree of consistency of viewpoint position across participants for both familiar and unfamiliar objects. In a second experiment, we found that these consistent, or canonical, views promoted better haptic recognition performance than other random views of the objects. Interestingly, these haptic canonical views were not necessarily the same as the canonical views normally found in visual perception. Nevertheless, our findings provide support for the idea that both the visual and the tactile systems are functionally equivalent in terms of how objects are represented in memory and subsequently recognised.
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Zelinsky, Gregory J., and Gregory L. Murphy. "Synchronizing Visual and Language Processing: An Effect of Object Name Length on Eye Movements." Psychological Science 11, no. 2 (March 2000): 125–31. http://dx.doi.org/10.1111/1467-9280.00227.

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Are visual and verbal processing systems functionally independent? Two experiments (one using line drawings of common objects, the other using faces) explored the relationship between the number of syllables in an object's name (one or three) and the visual inspection of that object. The tasks were short-term recognition and visual search. Results indicated more fixations and longer gaze durations on objects having three-syllable names when the task encouraged a verbal encoding of the objects (i.e., recognition). No effects of syllable length on eye movements were found when implicit naming demands were minimal (i.e., visual search). These findings suggest that implicitly naming a pictorial object constrains the oculomotor inspection of that object, and that the visual and verbal encoding of an object are synchronized so that the faster process must wait for the slower to be completed before gaze shifts to another object. Both findings imply a tight coupling between visual and linguistic processing, and highlight the utility of an oculomotor methodology to understand this coupling.
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Dissertations / Theses on the topic "Visual object recognition"

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Figueroa, Flores Carola. "Visual Saliency for Object Recognition, and Object Recognition for Visual Saliency." Doctoral thesis, Universitat Autònoma de Barcelona, 2021. http://hdl.handle.net/10803/671964.

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Per als humans, el reconeixement d’objectes és un procés gairebé instantani, precís i extremadament adaptable. A més, tenim la capacitat innata d’aprendre classes d’objectes nous a partir d’uns pocs exemples. El cervell humà redueix la complexitat de les dades entrants filtrant part de la informació i processant només aquelles coses que ens capturen l’atenció. Això, barrejat amb la nostra predisposició biològica per respondre a determinades formes o colors, ens permet reconèixer en un simple cop d’ull les regions més importants o destacades d’una imatge. Aquest mecanisme es pot observar analitzant sobre quines parts de les imatges hi posa l’atenció; on es fixen els ulls quan se’ls mostra una imatge. La forma més precisa de registrar aquest comportament és fer un seguiment dels moviments oculars mentre es mostren imatges. L’estimació computacional de la salubritat té com a objectiu identificar fins a quin punt les regions o els objectes destaquen respecte als seus entorns per als observadors humans. Els mapes Saliency es poden utilitzar en una àmplia gamma d’aplicacions, inclosa la detecció d’objectes, la compressió d’imatges i vídeos i el seguiment visual. La majoria de les investigacions en aquest camp s’han centrat en estimar automàticament els mapes de salubritat donats una imatge d’entrada. En el seu lloc, en aquesta tesi, ens proposem incorporar mapes de salubritat en una canalització de reconeixement d’objectes: volem investigar si els mapes de salubritat poden millorar els resultats del reconeixement d’objectes.En aquesta tesi, identifiquem diversos problemes relacionats amb l’estimació de la salubritat visual. En primer lloc, fins a quin punt es pot aprofitar l’estimació de la salubritat per millorar la formació d’un model de reconeixement d’objectes quan es disposa de dades d’entrenament escasses. Per solucionar aquest problema, dissenyem una xarxa de classificació d’imatges que incorpori informació d’informació salarial com a entrada. Aquesta xarxa processa el mapa de saliència a través d’una branca de xarxa dedicada i utilitza les característiques resultants per modular les característiques visuals estàndard de baix a dalt de l’entrada d’imatge original. Ens referirem a aquesta tècnica com a classificació d’imatges modulades en salinitat (SMIC). En amplis experiments sobre conjunts de dades de referència estàndard per al reconeixement d’objectes de gra fi, demostrem que la nostra arquitectura proposada pot millorar significativament el rendiment, especialment en el conjunt de dades amb dades de formació escasses.A continuació, abordem l’inconvenient principal de la canonada anterior: SMIC requereix un algorisme de saliència explícit que s’ha de formar en un conjunt de dades de saliència. Per solucionar-ho, implementem un mecanisme d’al·lucinació que ens permet incorporar la branca d’estimació de la salubritat en una arquitectura de xarxa neuronal entrenada de punta a punta que només necessita la imatge RGB com a entrada. Un efecte secundari d’aquesta arquitectura és l’estimació de mapes de salubritat. En experiments, demostrem que aquesta arquitectura pot obtenir resultats similars en reconeixement d’objectes com SMIC, però sense el requisit de mapes de salubritat de la veritat del terreny per entrenar el sistema. Finalment, hem avaluat la precisió dels mapes de salubritat que es produeixen com a efecte secundari del reconeixement d’objectes. Amb aquest propòsit, fem servir un conjunt de conjunts de dades de referència per a l’avaluació de la validesa basats en experiments de seguiment dels ulls. Sorprenentment, els mapes de salubritat estimats són molt similars als mapes que es calculen a partir d’experiments de rastreig d’ulls humans. Els nostres resultats mostren que aquests mapes de salubritat poden obtenir resultats competitius en els mapes de salubritat de referència. En un conjunt de dades de saliència sintètica, aquest mètode fins i tot obté l’estat de l’art sense la necessitat d’haver vist mai una imatge de saliència real.
El reconocimiento de objetos para los seres humanos es un proceso instantáneo, preciso y extremadamente adaptable. Además, tenemos la capacidad innata de aprender nuevas categorias de objetos a partir de unos pocos ejemplos. El cerebro humano reduce la complejidad de los datos entrantes filtrando parte de la información y procesando las cosas que captan nuestra atención. Esto, combinado con nuestra predisposición biológica a responder a determinadas formas o colores, nos permite reconocer en una simple mirada las regiones más importantes o destacadas de una imagen. Este mecanismo se puede observar analizando en qué partes de las imágenes los sujetos ponen su atención; por ejemplo donde fijan sus ojos cuando se les muestra una imagen. La forma más precisa de registrar este comportamiento es rastrear los movimientos de los ojos mientras se muestran imágenes. La estimación computacional del ‘saliency’, tiene como objetivo diseñar algoritmos que, dada una imagen de entrada, estimen mapas de ‘saliency’. Estos mapas se pueden utilizar en una variada gama de aplicaciones, incluida la detección de objetos, la compresión de imágenes y videos y el seguimiento visual. La mayoría de la investigación en este campo se ha centrado en estimar automáticamente estos mapas de ‘saliency’, dada una imagen de entrada. En cambio, en esta tesis, nos propusimos incorporar la estimación de ‘saliency’ en un procedimiento de reconocimiento de objeto, puesto que, queremos investigar si los mapas de ‘saliency’ pueden mejorar los resultados de la tarea de reconocimiento de objetos. En esta tesis, identificamos varios problemas relacionados con la estimación del ‘saliency’ visual. Primero, pudimos determinar en qué medida se puede aprovechar la estimación del ‘saliency’ para mejorar el entrenamiento de un modelo de reconocimiento de objetos cuando se cuenta con escasos datos de entrenamiento. Para resolver este problema, diseñamos una red de clasificación de imágenes que incorpora información de ‘saliency’ como entrada. Esta red procesa el mapa de ‘saliency’ a través de una rama de red dedicada y utiliza las características resultantes para modular las características visuales estándar ascendentes de la entrada de la imagen original. Nos referiremos a esta técnica como clasificación de imágenes moduladas por prominencia (SMIC en inglés). En numerosos experimentos realizando sobre en conjuntos de datos de referencia estándar para el reconocimiento de objetos ‘fine-grained’, mostramos que nuestra arquitectura propuesta puede mejorar significativamente el rendimiento, especialmente en conjuntos de datos con datos con escasos datos de entrenamiento. Luego, abordamos el principal inconveniente del problema anterior: es decir, SMIC requiere explícitamente un algoritmo de ‘saliency’, el cual debe entrenarse en un conjunto de datos de ‘saliency’. Para resolver esto, implementamos un mecanismo de alucinación que nos permite incorporar la rama de estimación de ‘saliency’ en una arquitectura de red neuronal entrenada de extremo a extremo que solo necesita la imagen RGB como entrada. Un efecto secundario de esta arquitectura es la estimación de mapas de ‘saliency’. En varios experimentos, demostramos que esta arquitectura puede obtener resultados similares en el reconocimiento de objetos como SMIC pero sin el requisito de mapas de ‘saliency’ para entrenar el sistema. Finalmente, evaluamos la precisión de los mapas de ‘saliency’ que ocurren como efecto secundario del reconocimiento de objetos. Para ello, utilizamos un de conjuntos de datos de referencia para la evaluación de la prominencia basada en experimentos de seguimiento ocular. Sorprendentemente, los mapas de ‘saliency’ estimados son muy similares a los mapas que se calculan a partir de experimentos de seguimiento ocular humano. Nuestros resultados muestran que estos mapas de ‘saliency’ pueden obtener resultados competitivos en mapas de ‘saliency’ de referencia.
For humans, the recognition of objects is an almost instantaneous, precise and extremely adaptable process. Furthermore, we have the innate capability to learn new object classes from only few examples. The human brain lowers the complexity of the incoming data by filtering out part of the information and only processing those things that capture our attention. This, mixed with our biological predisposition to respond to certain shapes or colors, allows us to recognize in a simple glance the most important or salient regions from an image. This mechanism can be observed by analyzing on which parts of images subjects place attention; where they fix their eyes when an image is shown to them. The most accurate way to record this behavior is to track eye movements while displaying images. Computational saliency estimation aims to identify to what extent regions or objects stand out with respect to their surroundings to human observers. Saliency maps can be used in a wide range of applications including object detection, image and video compression, and visual tracking. The majority of research in the field has focused on automatically estimating saliency maps given an input image. Instead, in this thesis, we set out to incorporate saliency maps in an object recognition pipeline: we want to investigate whether saliency maps can improve object recognition results. In this thesis, we identify several problems related to visual saliency estimation. First, to what extent the estimation of saliency can be exploited to improve the training of an object recognition model when scarce training data is available. To solve this problem, we design an image classification network that incorporates saliency information as input. This network processes the saliency map through a dedicated network branch and uses the resulting characteristics to modulate the standard bottom-up visual characteristics of the original image input. We will refer to this technique as saliency-modulated image classification (SMIC). In extensive experiments on standard benchmark datasets for fine-grained object recognition, we show that our proposed architecture can significantly improve performance, especially on dataset with scarce training data. Next, we address the main drawback of the above pipeline: SMIC requires an explicit saliency algorithm that must be trained on a saliency dataset. To solve this, we implement a hallucination mechanism that allows us to incorporate the saliency estimation branch in an end-to-end trained neural network architecture that only needs the RGB image as an input. A side-effect of this architecture is the estimation of saliency maps. In experiments, we show that this architecture can obtain similar results on object recognition as SMIC but without the requirement of ground truth saliency maps to train the system. Finally, we evaluated the accuracy of the saliency maps that occur as a side-effect of object recognition. For this purpose, we use a set of benchmark datasets for saliency evaluation based on eye-tracking experiments. Surprisingly, the estimated saliency maps are very similar to the maps that are computed from human eye-tracking experiments. Our results show that these saliency maps can obtain competitive results on benchmark saliency maps. On one synthetic saliency dataset this method even obtains the state-of-the-art without the need of ever having seen an actual saliency image for training.
Universitat Autònoma de Barcelona. Programa de Doctorat en Informàtica
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Fergus, Robert. "Visual object category recognition." Thesis, University of Oxford, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.425029.

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Wallenberg, Marcus. "Embodied Visual Object Recognition." Doctoral thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-132762.

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Object recognition is a skill we as humans often take for granted. Due to our formidable object learning, recognition and generalisation skills, it is sometimes hard to see the multitude of obstacles that need to be overcome in order to replicate this skill in an artificial system. Object recognition is also one of the classical areas of computer vision, and many ways of approaching the problem have been proposed. Recently, visually capable robots and autonomous vehicles have increased the focus on embodied recognition systems and active visual search. These applications demand that systems can learn and adapt to their surroundings, and arrive at decisions in a reasonable amount of time, while maintaining high object recognition performance. This is especially challenging due to the high dimensionality of image data. In cases where end-to-end learning from pixels to output is needed, mechanisms designed to make inputs tractable are often necessary for less computationally capable embodied systems.Active visual search also means that mechanisms for attention and gaze control are integral to the object recognition procedure. Therefore, the way in which attention mechanisms should be introduced into feature extraction and estimation algorithms must be carefully considered when constructing a recognition system.This thesis describes work done on the components necessary for creating an embodied recognition system, specifically in the areas of decision uncertainty estimation, object segmentation from multiple cues, adaptation of stereo vision to a specific platform and setting, problem-specific feature selection, efficient estimator training and attentional modulation in convolutional neural networks. Contributions include the evaluation of methods and measures for predicting the potential uncertainty reduction that can be obtained from additional views of an object, allowing for adaptive target observations. Also, in order to separate a specific object from other parts of a scene, it is often necessary to combine multiple cues such as colour and depth in order to obtain satisfactory results. Therefore, a method for combining these using channel coding has been evaluated. In order to make use of three-dimensional spatial structure in recognition, a novel stereo vision algorithm extension along with a framework for automatic stereo tuning have also been investigated. Feature selection and efficient discriminant sampling for decision tree-based estimators have also been implemented. Finally, attentional multi-layer modulation of convolutional neural networks for recognition in cluttered scenes has been evaluated. Several of these components have been tested and evaluated on a purpose-built embodied recognition platform known as Eddie the Embodied.
Embodied Visual Object Recognition
FaceTrack
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Breuel, Thomas M. "Geometric Aspects of Visual Object Recognition." Thesis, Massachusetts Institute of Technology, 1992. http://hdl.handle.net/1721.1/7342.

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This thesis presents there important results in visual object recognition based on shape. (1) A new algorithm (RAST; Recognition by Adaptive Sudivisions of Tranformation space) is presented that has lower average-case complexity than any known recognition algorithm. (2) It is shown, both theoretically and empirically, that representing 3D objects as collections of 2D views (the "View-Based Approximation") is feasible and affects the reliability of 3D recognition systems no more than other commonly made approximations. (3) The problem of recognition in cluttered scenes is considered from a Bayesian perspective; the commonly-used "bounded-error errorsmeasure" is demonstrated to correspond to an independence assumption. It is shown that by modeling the statistical properties of real-scenes better, objects can be recognized more reliably.
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Meger, David Paul. "Visual object recognition for mobile platforms." Thesis, University of British Columbia, 2013. http://hdl.handle.net/2429/44682.

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A robot must recognize objects in its environment in order to complete numerous tasks. Significant progress has been made in modeling visual appearance for image recognition, but the performance of current state-of-the-art approaches still falls short of that required by applications. This thesis describes visual recognition methods that leverage the spatial information sources available on-board mobile robots, such as the position of the platform in the world and the range data from its sensors, in order to significantly improve performance. Our research includes: a physical robotic platform that is capable of state-of-the-art recognition performance; a re-usable data set that facilitates study of the robotic recognition problem by the scientific community; and a three dimensional object model that demonstrates improved robustness to clutter. Based on our 3D model, we describe algorithms that integrate information across viewpoints, relate objects to auxiliary 3D sensor information, plan paths to next-best-views, explicitly model object occlusions and reason about the sub-parts of objects in 3D. Our approaches have been proven experimentally on-board the Curious George robot platform, which placed first in an international object recognition challenge for mobile robots for several years. We have also collected a large set of visual experiences from a robot, annotated the true objects in this data and made it public to the research community for use in performance evaluation. A path planning system derived from our model has been shown to hasten confident recognition by allowing informative viewpoints to be observed quickly. In each case studied, our system demonstrates significant improvements in recognition rate, in particular on realistic cluttered scenes, which promises more successful task execution for robotic platforms in the future.
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Mahmood, Hamid. "Visual Attention-based Object Detection and Recognition." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-94024.

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This thesis is all about the visual attention, starting from understanding the human visual system up till applying this mechanism to a real-world computer vision application. This has been achieved by taking the advantage of latest findings about the human visual attention and the increased performance of the computers. These two facts played a vital role in simulating the many different aspects of this visual behavior. In addition, the concept of bio-inspired visual attention systems have become applicable due to the emergence of different interdisciplinary approaches to vision which leads to a beneficial interaction between the scientists related to different fields. The problems of high complexities in computer vision lead to consider the visual attention paradigm to become a part of real time computer vision solutions which have increasing demand.  In this thesis work, different aspects of visual attention paradigm have been dealt ranging from the biological modeling to the real-world computer vision tasks implementation based on this visual behavior. The implementation of traffic signs detection and recognition system benefited from this mechanism is the central part of this thesis work.
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Villalba, Michael Joseph. "Fast visual recognition of large object sets." Thesis, Massachusetts Institute of Technology, 1990. http://hdl.handle.net/1721.1/42211.

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Lindqvist, Zebh. "Design Principles for Visual Object Recognition Systems." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-80769.

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Today's smartphones are capable of accomplishing far more advanced tasks than reading emails. With the modern framework TensorFlow, visual object recognition becomes possible using smartphone resources. This thesis shows that the main challenge does not lie in developing an artifact which performs visual object recognition. Instead, the main challenge lies in developing an ecosystem which allows for continuous improvement of the system’s ability to accomplish the given task without laborious and inefficient data collection. This thesis presents four design principles which contribute to an efficient ecosystem with quick initiation of new object classes and efficient data collection which is used to continuously improve the system’s ability to recognize smart meters in varying environments in an automated fashion.
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Teynor, Alexandra. "Visual object class recognition using local descriptions." [S.l. : s.n.], 2008. http://nbn-resolving.de/urn:nbn:de:bsz:25-opus-62371.

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Pemula, Latha. "Low-shot Visual Recognition." Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/73321.

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Many real world datasets are characterized by having a long tailed distribution, with several samples for some classes and only a few samples for other classes. While many Deep Learning based solutions exist for object recognition when hundreds of samples are available, there are not many solutions for the case when there are only a few samples available per class. Recognition in the regime where the number of training samples available for each class are low, ranging from 1 to couple of tens of examples is called Lowshot Recognition. In this work, we attempt to solve this problem. Our framework is similar to [1]. We use a related dataset with sufficient number (a couple of hundred) of samples per class to learn representations using a Convolutional Neural Network (CNN). This CNN is used to extract features of the lowshot samples and learn a classifier . During representation learning, we enforce the learnt representations to obey certain property by using a custom loss function. We believe that when the lowshot sample obey this property the classification step becomes easier. We show that the proposed solution performs better than the softmax classifier by a good margin.
Master of Science
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Books on the topic "Visual object recognition"

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Grauman, Kristen, and Bastian Leibe. Visual Object Recognition. Cham: Springer International Publishing, 2011. http://dx.doi.org/10.1007/978-3-031-01553-3.

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Bastian, Leibe, ed. Visual object recognition. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2011.

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3

Ullman, Shimon. High-level vision: Object recognition and visual cognition. Cambridge, Mass: MIT Press, 1996.

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Ullman, Shimon. High-level vision: Object recognition and visual cognition. Cambridge, Mass: MIT Press, 2000.

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Poth, Christian H. Episodic visual cognition: Implications for object and short-term recognition. Bielefeld: Universitätsbibliothek Bielefeld, 2017.

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6

Farah, Martha J. Visual agnosia: Disorders of object recognition and what they tell us about normal vision. Cambridge, Mass: MIT Press, 1990.

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Visual agnosia: Disorders of object recognition and what they tell us about normal vision. Cambridge, Mass: MIT Press, 1990.

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Visual agnosia: Disorders of object recognition and what they tell us about normal vision. Cambridge, Mass: MIT Press, 1999.

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Farah, Martha J. Visual agnosia: Disorders of object recognition and what they tell us about normal vision. Cambridge, Mass: MIT Press, 1995.

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Information routing, correspondence finding, and object recognition in the brain. Berlin: Springer-Verlag, 2010.

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Book chapters on the topic "Visual object recognition"

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Humphreys, Glyn W., and Vicki Bruce. "Visual Object Recognition." In Visual Cognition, 51–101. London: Psychology Press, 2021. http://dx.doi.org/10.4324/9781315785141-3.

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Gong, Shengrong, Chunping Liu, Yi Ji, Baojiang Zhong, Yonggang Li, and Husheng Dong. "Visual Object Recognition." In Advanced Image and Video Processing Using MATLAB, 351–87. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77223-3_10.

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Tomita, Fumiaki, and Saburo Tsuji. "Object Recognition." In Computer Analysis of Visual Textures, 115–35. Boston, MA: Springer US, 1990. http://dx.doi.org/10.1007/978-1-4613-1553-7_8.

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Grauman, Kristen, and Bastian Leibe. "Generic Object Detection: Finding and Scoring Candidates." In Visual Object Recognition, 79–86. Cham: Springer International Publishing, 2011. http://dx.doi.org/10.1007/978-3-031-01553-3_9.

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Grauman, Kristen, and Bastian Leibe. "Example Systems: Generic Object Recognition." In Visual Object Recognition, 103–18. Cham: Springer International Publishing, 2011. http://dx.doi.org/10.1007/978-3-031-01553-3_11.

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Grauman, Kristen, and Bastian Leibe. "Introduction." In Visual Object Recognition, 1–5. Cham: Springer International Publishing, 2011. http://dx.doi.org/10.1007/978-3-031-01553-3_1.

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Grauman, Kristen, and Bastian Leibe. "Overview: Recognition of Generic Object Categories." In Visual Object Recognition, 61. Cham: Springer International Publishing, 2011. http://dx.doi.org/10.1007/978-3-031-01553-3_7.

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Grauman, Kristen, and Bastian Leibe. "Overview: Recognition of Specific Objects." In Visual Object Recognition, 7–10. Cham: Springer International Publishing, 2011. http://dx.doi.org/10.1007/978-3-031-01553-3_2.

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Grauman, Kristen, and Bastian Leibe. "Example Systems: Specific-Object Recognition." In Visual Object Recognition, 55–61. Cham: Springer International Publishing, 2011. http://dx.doi.org/10.1007/978-3-031-01553-3_6.

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Grauman, Kristen, and Bastian Leibe. "Learning Generic Object Category Models." In Visual Object Recognition, 87–102. Cham: Springer International Publishing, 2011. http://dx.doi.org/10.1007/978-3-031-01553-3_10.

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Conference papers on the topic "Visual object recognition"

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Zhu, Zhuotun, Lingxi Xie, and Alan Yuille. "Object Recognition with and without Objects." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/505.

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While recent deep neural networks have achieved a promising performance on object recognition, they rely implicitly on the visual contents of the whole image. In this paper, we train deep neural networks on the foreground (object) and background (context) regions of images respectively. Considering human recognition in the same situations, networks trained on the pure background without objects achieves highly reasonable recognition performance that beats humans by a large margin if only given context. However, humans still outperform networks with pure object available, which indicates networks and human beings have different mechanisms in understanding an image. Furthermore, we straightforwardly combine multiple trained networks to explore different visual cues learned by different networks. Experiments show that useful visual hints can be explicitly learned separately and then combined to achieve higher performance, which verifies the advantages of the proposed framework.
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Pelli, Denis G. "Visual Sensitivity and Object Recognition." In Frontiers in Optics. Washington, D.C.: OSA, 2016. http://dx.doi.org/10.1364/fio.2016.fth4i.2.

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Walmsley, Nicholas P., and K. M. Curtis. "Robust object recognition using symmetry." In Visual Communications and Image Processing '94, edited by Aggelos K. Katsaggelos. SPIE, 1994. http://dx.doi.org/10.1117/12.186001.

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Mamic, George J., and Mohammed Bennamoun. "Review of 3D object representation techniques for automatic object recognition." In Visual Communications and Image Processing 2000, edited by King N. Ngan, Thomas Sikora, and Ming-Ting Sun. SPIE, 2000. http://dx.doi.org/10.1117/12.386708.

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Zhang, Mengmi, Claire Tseng, and Gabriel Kreiman. "Putting Visual Object Recognition in Context." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. http://dx.doi.org/10.1109/cvpr42600.2020.01300.

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Chao Zhu, Charles-Edmond Bichot, and Liming Chen. "Visual object recognition using DAISY descriptor." In 2011 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2011. http://dx.doi.org/10.1109/icme.2011.6011957.

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Marszalek, Marcin, and Cordelia Schmid. "Semantic Hierarchies for Visual Object Recognition." In 2007 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2007. http://dx.doi.org/10.1109/cvpr.2007.383272.

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Yao, Peng, Yongtian Wang, Can Chen, Dongdong Weng, and Yue Liu. "Dangerous object recognition for visual surveillance." In 2012 International Conference on Audio, Language and Image Processing (ICALIP). IEEE, 2012. http://dx.doi.org/10.1109/icalip.2012.6376587.

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Tian, X., H. Deng, K. Yamazaki, M. Fujishima, and M. Mori. "On-Machine Visual Modeling System With Object Recognition." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-81661.

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This paper presents an on-machine modeling system that tries to bridge the gap between the design and the machining. This system is able to build a comprehensive solid model of the CNC machining workspace after the workpiece and fixtures have been installed onto the working table. This solid model can be used for simulation to enhance its credibility. For this purpose, one prototype of a 3D visual modeling system is proposed and designed. In order to accurately calibrate CCD cameras upon the absolute coordinate frame of the machining center, a practical calibration method is presented at first. To segment the target part and extract its 2D features on the captured images, the techniques of Image Decomposition and a modified Standard Hough Transform (SHT) are designed. Using these 2D features, the 3D visual stereovision system, powered by a designed feature matching engine, is capable of obtaining the 3D features of the target part. Furthermore, the part has been identified by the object recognition technology. This recognition includes part recognition and pose recognition. In the part recognition, the part is recognized and an initial pose transform is obtained. Using this initial pose transform, the pose optimization method, named as Dual Iterative Closest Lines (DICL), is designed to locate the optimum position and orientation of the solid model of the recognized part. Finally, this modeling system is tested on a machining center. The experimental result indicates the innovation and feasibility of the proposed modeling system.
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Ghali, Ahmed, Andrew S. Cunningham, and Tony P. Pridmore. "Object and event recognition for stroke rehabilitation." In Visual Communications and Image Processing 2003, edited by Touradj Ebrahimi and Thomas Sikora. SPIE, 2003. http://dx.doi.org/10.1117/12.503470.

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Reports on the topic "Visual object recognition"

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Breuel, Thomas M. Geometric Aspects of Visual Object Recognition. Fort Belvoir, VA: Defense Technical Information Center, May 1992. http://dx.doi.org/10.21236/ada259454.

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Edelman, Shimon, Heinrich H. Buelthoff, and Erik Sklar. Task and Object Learning in Visual Recognition. Fort Belvoir, VA: Defense Technical Information Center, January 1991. http://dx.doi.org/10.21236/ada259961.

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Farah, Martha J. The Functional Architecture of Visual Object Recognition. Fort Belvoir, VA: Defense Technical Information Center, July 1991. http://dx.doi.org/10.21236/ada238617.

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Jacobs, David W. The Use of Grouping in Visual Object Recognition. Fort Belvoir, VA: Defense Technical Information Center, October 1988. http://dx.doi.org/10.21236/ada201691.

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Serre, Thomas, Lior Wolf, and Tomaso Poggio. Object Recognition with Features Inspired by Visual Cortex. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada454604.

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Tarr, Michael J. Presentations of Shape in Object Recognition and Long-Term Visual Memory. Fort Belvoir, VA: Defense Technical Information Center, April 1994. http://dx.doi.org/10.21236/ada281336.

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Farah, Martha J. The Functional Architecture Of Visual Object Recognition: Cognitive And Neuropsychological Approaches. Fort Belvoir, VA: Defense Technical Information Center, December 1992. http://dx.doi.org/10.21236/ada259859.

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Tarr, Michael J. Representations of Shape in Object Recognition and Long-Term Visual Memory. Fort Belvoir, VA: Defense Technical Information Center, June 1996. http://dx.doi.org/10.21236/ada310172.

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Tarr, Michael J. Representations of Shape in Object Recognition and Long-Term Visual Memory. Fort Belvoir, VA: Defense Technical Information Center, February 1993. http://dx.doi.org/10.21236/ada264342.

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Gonzales, Antonio Ignacio, Paul C. Reeves, John J. Jones, and Benjamin D. Farkas. ROCIT : a visual object recognition algorithm based on a rank-order coding scheme. Office of Scientific and Technical Information (OSTI), June 2004. http://dx.doi.org/10.2172/919190.

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