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Journal articles on the topic 'Hand-crafted'

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Published: 4 June 2021
Last updated: 1 February 2022

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1

Gray, Katherine. "Hand Crafted: Driftwood Horse." Journal of Hand Surgery 40, no. 5 (May 2015): 1007. http://dx.doi.org/10.1016/j.jhsa.2014.12.033.

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2

Tokarczyk, P., J. D. Wegner, S. Walk, and K. Schindler. "BEYOND HAND-CRAFTED FEATURES IN REMOTE SENSING." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-3/W1 (May 16, 2013): 35–40. http://dx.doi.org/10.5194/isprsannals-ii-3-w1-35-2013.

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3

Hartshorne, Nikolas J., Donald T. Reay, and Richard C. Harruff. "Accidental Firearm Fatality Involving a Hand-Crafted Pen Gun." American Journal of Forensic Medicine and Pathology 18, no. 1 (March 1997): 92–95. http://dx.doi.org/10.1097/00000433-199703000-00017.

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4

Basirat, Ali, and Heshaam Faili. "Bridge the gap between statistical and hand-crafted grammars." Computer Speech & Language 27, no. 5 (August 2013): 1085–104. http://dx.doi.org/10.1016/j.csl.2013.02.001.

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5

Zhang, Tianwen, and Xiaoling Zhang. "Injection of Traditional Hand-Crafted Features into Modern CNN-Based Models for SAR Ship Classification: What, Why, Where, and How." Remote Sensing 13, no. 11 (May 26, 2021): 2091. http://dx.doi.org/10.3390/rs13112091.

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With the rise of artificial intelligence, many advanced Synthetic Aperture Radar (SAR) ship classifiers based on convolutional neural networks (CNNs) have achieved better accuracies than traditional hand-crafted feature ones. However, most existing CNN-based models uncritically abandon traditional hand-crafted features, and rely excessively on abstract ones of deep networks. This may be controversial, potentially creating challenges to improve classification performance further. Therefore, in view of this situation, this paper explores preliminarily the possibility of injection of traditional hand-crafted features into modern CNN-based models to further improve SAR ship classification accuracy. Specifically, we will—(1) illustrate what this injection technique is, (2) explain why it is needed, (3) discuss where it should be applied, and (4) describe how it is implemented. Experimental results on the two open three-category OpenSARShip-1.0 and seven-category FUSAR-Ship datasets indicate that it is effective to perform injection of traditional hand-crafted features into CNN-based models to improve classification accuracy. Notably, the maximum accuracy improvement reaches 6.75%. Hence, we hold the view that it is not advisable to abandon uncritically traditional hand-crafted features, because they can also play an important role in CNN-based models.
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Siegel, Dennis, Christian Kraetzer, Stefan Seidlitz, and Jana Dittmann. "Media Forensics Considerations on DeepFake Detection with Hand-Crafted Features." Journal of Imaging 7, no. 7 (July 1, 2021): 108. http://dx.doi.org/10.3390/jimaging7070108.

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DeepFake detection is a novel task for media forensics and is currently receiving a lot of research attention due to the threat these targeted video manipulations propose to the trust placed in video footage. The current trend in DeepFake detection is the application of neural networks to learn feature spaces that allow them to be distinguished from unmanipulated videos. In this paper, we discuss, with features hand-crafted by domain experts, an alternative to this trend. The main advantage that hand-crafted features have over learned features is their interpretability and the consequences this might have for plausibility validation for decisions made. Here, we discuss three sets of hand-crafted features and three different fusion strategies to implement DeepFake detection. Our tests on three pre-existing reference databases show detection performances that are under comparable test conditions (peak AUC > 0.95) to those of state-of-the-art methods using learned features. Furthermore, our approach shows a similar, if not better, generalization behavior than neural network-based methods in tests performed with different training and test sets. In addition to these pattern recognition considerations, first steps of a projection onto a data-centric examination approach for forensics process modeling are taken to increase the maturity of the present investigation.
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Cahill, Aoife, Michael Burke, Ruth O'Donovan, Stefan Riezler, Josef van Genabith, and Andy Way. "Wide-Coverage Deep Statistical Parsing Using Automatic Dependency Structure Annotation." Computational Linguistics 34, no. 1 (March 2008): 81–124. http://dx.doi.org/10.1162/coli.2008.34.1.81.

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A number of researchers have recently conducted experiments comparing “deep” hand-crafted wide-coverage with “shallow” treebank- and machine-learning-based parsers at the level of dependencies, using simple and automatic methods to convert tree output generated by the shallow parsers into dependencies. In this article, we revisit such experiments, this time using sophisticated automatic LFG f-structure annotation methodologies with surprising results. We compare various PCFG and history-based parsers to find a baseline parsing system that fits best into our automatic dependency structure annotation technique. This combined system of syntactic parser and dependency structure annotation is compared to two hand-crafted, deep constraint-based parsers, RASP and XLE. We evaluate using dependency-based gold standards and use the Approximate Randomization Test to test the statistical significance of the results. Our experiments show that machine-learning-based shallow grammars augmented with sophisticated automatic dependency annotation technology outperform hand-crafted, deep, wide-coverage constraint grammars. Currently our best system achieves an f-score of 82.73% against the PARC 700 Dependency Bank, a statistically significant improvement of 2.18% over the most recent results of 80.55% for the hand-crafted LFG grammar and XLE parsing system and an f-score of 80.23% against the CBS 500 Dependency Bank, a statistically significant 3.66% improvement over the 76.57% achieved by the hand-crafted RASP grammar and parsing system.
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8

TAKAHASHI, KAZUKO, HIROYA TAKAMURA, and MANABU OKUMURA. "Automatic Occupation Coding with Machine Learning and Hand-Crafted Rules." Journal of Natural Language Processing 12, no. 2 (2005): 3–23. http://dx.doi.org/10.5715/jnlp.12.2_3.

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9

Cambra‐Fierro, Jesus, Rosario Vazquez‐Carrasco, and Edgar Centeno. "The challenges of internationalising national culture‐based hand‐crafted products." Marketing Intelligence & Planning 27, no. 7 (October 23, 2009): 900–908. http://dx.doi.org/10.1108/02634500911000216.

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10

Keçeli, Ali Seydi, Aydın Kaya, and Seda Uzunçimen Keçeli. "Classification of radiolarian images with hand-crafted and deep features." Computers & Geosciences 109 (December 2017): 67–74. http://dx.doi.org/10.1016/j.cageo.2017.08.011.

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11

Remondino, F., F. Menna, and L. Morelli. "EVALUATING HAND-CRAFTED AND LEARNING-BASED FEATURES FOR PHOTOGRAMMETRIC APPLICATIONS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2021 (June 28, 2021): 549–56. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2021-549-2021.

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Abstract. The image orientation (or Structure from Motion – SfM) process needs well localized, repeatable and stable tie points in order to derive camera poses and a sparse 3D representation of the surveyed scene. The accurate identification of tie points in large image datasets is still an open research topic in the photogrammetric and computer vision communities. Tie points are established by firstly extracting keypoint using a hand-crafted feature detector and descriptor methods. In the last years new solutions, based on convolutional neural network (CNN) methods, were proposed to let a deep network discover which feature extraction process and representation are most suitable for the processed images. In this paper we aim to compare state-of-the-art hand-crafted and learning-based method for the establishment of tie points in various and different image datasets. The investigation highlights the actual challenges for feature matching and evaluates selected methods under different acquisition conditions (network configurations, image overlap, UAV vs terrestrial, strip vs convergent) and scene's characteristics. Remarks and lessons learned constrained to the used datasets and methods are provided.
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12

Taha Ahmed, Ismail, Baraa Tareq Hammad, and Norziana Jamil. "A comparative analysis of image copy-move forgery detection algorithms based on hand and machine-crafted features." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 2 (May 1, 2021): 1177. http://dx.doi.org/10.11591/ijeecs.v22.i2.pp1177-1190.

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<span>Digital image forgery (DIF) is the act of deliberate alteration of an image to change the details transmitted by it. The manipulation may either add, delete or alter any of the image features or contents, without leaving any hint of the change induced. In general, copy-move forgery, also referred to as replication, is the most common of the various kinds of passive image forgery techniques. In the copy-move forgery, the basic process is copy/paste from one area to another in the same image. Over the past few decades various image copy-move forgery detection (IC-MFDs) surveys have been existed. However, these surveys are not covered for both IC-MFD algorithms based hand-crafted features and IC-MFDs algorithms based machine-crafted features. Therefore, The paper presented a comparative analysis of IC-MFDs by collect various types of IC-MFDs and group them rely on their features used. Two groups, i.e. IC-MFDs based hand-crafted features and IC-MFDs based machine-crafted features. IC-MFD algorithms based hand-crafted features are the algorithms that detect the faked image depending on manual feature extraction while IC-MFD algorithms based machine-crafted features are the algorithms that detect the faked image automatically from image. Our hope that this presented analysis will to keep up-to-date the researchers in the field of IC-MFD.</span>
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13

Ikuta, Shigeru, Takahiro Endo, Jinko Tomiyama, Ryoichi Ishitobi, Naoki Sakai, Kyoko Itakura, Saori Fujieda, et al. "Hand-crafted teaching materials and school activities in collaboration with schoolteachers." International Journal of Human Culture Studies 2018, no. 28 (January 1, 2018): 137–78. http://dx.doi.org/10.9748/hcs.2018.137.

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14

Li, Xue, Xin Ma, and Peng Song. "Fusion of Deep Feature and Hand-Crafted Features for Terrain Recognition." IOP Conference Series: Materials Science and Engineering 646 (October 17, 2019): 012052. http://dx.doi.org/10.1088/1757-899x/646/1/012052.

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15

Okkabaz, Nuri, Eren Esen, David M. Schwartzberg, Feza H. Remzi, and Hasan T. Kirat. "Hand-Crafted Endoluminal Vacuum-Assisted Drainage for Anastomotic Leak After IPAA." Diseases of the Colon & Rectum 62, no. 10 (October 2019): 1259–62. http://dx.doi.org/10.1097/dcr.0000000000001453.

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16

Bello-Cerezo, Raquel, Francesco Bianconi, Francesco Di Maria, Paolo Napoletano, and Fabrizio Smeraldi. "Comparative Evaluation of Hand-Crafted Image Descriptors vs. Off-the-Shelf CNN-Based Features for Colour Texture Classification under Ideal and Realistic Conditions." Applied Sciences 9, no. 4 (February 20, 2019): 738. http://dx.doi.org/10.3390/app9040738.

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Convolutional Neural Networks (CNN) have brought spectacular improvements in several fields of machine vision including object, scene and face recognition. Nonetheless, the impact of this new paradigm on the classification of fine-grained images—such as colour textures—is still controversial. In this work, we evaluate the effectiveness of traditional, hand-crafted descriptors against off-the-shelf CNN-based features for the classification of different types of colour textures under a range of imaging conditions. The study covers 68 image descriptors (35 hand-crafted and 33 CNN-based) and 46 compilations of 23 colour texture datasets divided into 10 experimental conditions. On average, the results indicate a marked superiority of deep networks, particularly with non-stationary textures and in the presence of multiple changes in the acquisition conditions. By contrast, hand-crafted descriptors were better at discriminating stationary textures under steady imaging conditions and proved more robust than CNN-based features to image rotation.
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17

Paladini, Emanuela, Edoardo Vantaggiato, Fares Bougourzi, Cosimo Distante, Abdenour Hadid, and Abdelmalik Taleb-Ahmed. "Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification." Journal of Imaging 7, no. 3 (March 9, 2021): 51. http://dx.doi.org/10.3390/jimaging7030051.

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In recent years, automatic tissue phenotyping has attracted increasing interest in the Digital Pathology (DP) field. For Colorectal Cancer (CRC), tissue phenotyping can diagnose the cancer and differentiate between different cancer grades. The development of Whole Slide Images (WSIs) has provided the required data for creating automatic tissue phenotyping systems. In this paper, we study different hand-crafted feature-based and deep learning methods using two popular multi-classes CRC-tissue-type databases: Kather-CRC-2016 and CRC-TP. For the hand-crafted features, we use two texture descriptors (LPQ and BSIF) and their combination. In addition, two classifiers are used (SVM and NN) to classify the texture features into distinct CRC tissue types. For the deep learning methods, we evaluate four Convolutional Neural Network (CNN) architectures (ResNet-101, ResNeXt-50, Inception-v3, and DenseNet-161). Moreover, we propose two Ensemble CNN approaches: Mean-Ensemble-CNN and NN-Ensemble-CNN. The experimental results show that the proposed approaches outperformed the hand-crafted feature-based methods, CNN architectures and the state-of-the-art methods in both databases.
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18

Tarchoun, Bilel, Anouar Khalifa, Selma Dhifallah, Imen Jegham, and Mohamed Mahjou. "Hand-Crafted Features vs Deep Learning for Pedestrian Detection in Moving Camera." Traitement du Signal 37, no. 2 (April 30, 2020): 209–16. http://dx.doi.org/10.18280/ts.370206.

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19

Denaro, Gianni. "Industry 4.0 and New Artisans: Between Hand-crafted Design and Digital Production." International Journal of Design Management and Professional Practice 14, no. 3 (2020): 1–7. http://dx.doi.org/10.18848/2325-162x/cgp/v14i03/1-7.

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20

Wierzba, Leanne. "What Is Luxury?: Curating Connections between the Hand-crafted and Global Industry." Luxury 2, no. 1 (January 2015): 9–23. http://dx.doi.org/10.1080/20511817.2015.11428562.

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21

Fu, Zhenqi, Feng Shao, Qiuping Jiang, Randi Fu, and Yo-Sung Ho. "Quality Assessment of Retargeted Images Using Hand-Crafted and Deep-Learned Features." IEEE Access 6 (2018): 12008–18. http://dx.doi.org/10.1109/access.2018.2808322.

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22

Su, Ran, Tianling Liu, Changming Sun, Qiangguo Jin, Rachid Jennane, and Leyi Wei. "Fusing convolutional neural network features with hand-crafted features for osteoporosis diagnoses." Neurocomputing 385 (April 2020): 300–309. http://dx.doi.org/10.1016/j.neucom.2019.12.083.

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23

Manivannan, Siyamalan, Wenqi Li, Jianguo Zhang, Emanuele Trucco, and Stephen J. McKenna. "Structure Prediction for Gland Segmentation With Hand-Crafted and Deep Convolutional Features." IEEE Transactions on Medical Imaging 37, no. 1 (January 2018): 210–21. http://dx.doi.org/10.1109/tmi.2017.2750210.

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24

Howarth, Lynne C., and Emma Knight. "To Every Artifact Its Voice: Creating Surrogates for Hand-Crafted Indigenous Objects." Cataloging & Classification Quarterly 53, no. 5-6 (July 4, 2015): 580–95. http://dx.doi.org/10.1080/01639374.2015.1008719.

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Levinshtein, Alex, Edmund Phung, and Parham Aarabi. "Hybrid eye center localization using cascaded regression and hand-crafted model fitting." Image and Vision Computing 71 (March 2018): 17–24. http://dx.doi.org/10.1016/j.imavis.2018.01.003.

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Abdellatef, Essam, Eman M. Omran, Randa F. Soliman, Nabil A. Ismail, Salah Eldin S. E. Abd Elrahman, Khalid N. Ismail, Mohamed Rihan, Fathi E. Abd El-Samie, and Ayman A. Eisa. "Fusion of deep-learned and hand-crafted features for cancelable recognition systems." Soft Computing 24, no. 20 (April 13, 2020): 15189–208. http://dx.doi.org/10.1007/s00500-020-04856-1.

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27

Saba, Tanzila, Ahmed Sameh Mohamed, Mohammad El-Affendi, Javeria Amin, and Muhammad Sharif. "Brain tumor detection using fusion of hand crafted and deep learning features." Cognitive Systems Research 59 (January 2020): 221–30. http://dx.doi.org/10.1016/j.cogsys.2019.09.007.

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Papakostas, Michalis, Evaggelos Spyrou, Theodoros Giannakopoulos, Giorgos Siantikos, Dimitrios Sgouropoulos, Phivos Mylonas, and Fillia Makedon. "Deep Visual Attributes vs. Hand-Crafted Audio Features on Multidomain Speech Emotion Recognition." Computation 5, no. 4 (June 1, 2017): 26. http://dx.doi.org/10.3390/computation5020026.

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29

Sharma, Kaamya. "Hand-crafted identities: Sartorial taste and belonging amongst elite women in urban India." Journal of Material Culture 25, no. 1 (May 10, 2019): 60–75. http://dx.doi.org/10.1177/1359183519846155.

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Craft in contemporary India is a loaded signifier of the handmade, the vernacular, the traditional, and the authentic. Handloom saris, as part of a pan-Indianized repertoire, index the virtues of traditional femininity, local patronage and craft-based aesthetics. However, a decline in their consumption has led to revival projects centred on addressing concerns of livelihoods, markets, and consumer identities. This article presents a historically contextualized study of elite women who are engaged in handloom sari revival. Through an analysis of their sartorial preferences and interventions in handloom revival, the author suggests that their involvement in the field of craft is a performative strategy that consolidates their elite status. These actors affirm belonging in elite spaces using the discourses of crafts difference, biomoral consumption, and aesthetic taste. Moreover, revival is always presented as on the verge of happening but never actually achieved, justifying the need for sustained elite intervention, in turn cementing elite status.
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Alwaely, Basheer, and Charith Abhayaratne. "AGSF: Adaptive Graph Formulation and Hand-Crafted Graph Spectral Features for Shape Representation." IEEE Access 8 (2020): 182260–72. http://dx.doi.org/10.1109/access.2020.3028696.

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de Chazal, Philip, and Nadi Sadr. "Automatic scoring of non-apnoea arousals using hand-crafted features from the polysomnogram." Physiological Measurement 40, no. 12 (December 27, 2019): 124001. http://dx.doi.org/10.1088/1361-6579/ab5ed3.

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Jdid, Bachir, Wei Hong Lim, Iyad Dayoub, Kais Hassan, and Mohd Rizon Bin Mohamed Juhari. "Robust Automatic Modulation Recognition Through Joint Contribution of Hand-Crafted and Contextual Features." IEEE Access 9 (2021): 104530–46. http://dx.doi.org/10.1109/access.2021.3099222.

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33

BASIRAT, A., H. FAILI, and J. NIVRE. "A statistical model for grammar mapping." Natural Language Engineering 22, no. 2 (February 20, 2015): 215–55. http://dx.doi.org/10.1017/s1351324915000017.

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AbstractThe two main classes of grammars are (a) hand-crafted grammars, which are developed by language experts, and (b) data-driven grammars, which are extracted from annotated corpora. This paper introduces a statistical method for mapping the elementary structures of a data-driven grammar onto the elementary structures of a hand-crafted grammar in order to combine their advantages. The idea is employed in the context of Lexicalized Tree-Adjoining Grammars (LTAG) and tested on two LTAGs of English: the hand-crafted LTAG developed in the XTAG project, and the data-driven LTAG, which is automatically extracted from the Penn Treebank and used by the MICA parser. We propose a statistical model for mapping any elementary tree sequence of the MICA grammar onto a proper elementary tree sequence of the XTAG grammar. The model has been tested on three subsets of the WSJ corpus that have average lengths of 10, 16, and 18 words, respectively. The experimental results show that full-parse trees with average F1-scores of 72.49, 64.80, and 62.30 points could be built from 94.97%, 96.01%, and 90.25% of the XTAG elementary tree sequences assigned to the subsets, respectively. Moreover, by reducing the amount of syntactic lexical ambiguity of sentences, the proposed model significantly improves the efficiency of parsing in the XTAG system.
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Panigrahi, Sweta, and U. S. N. Raju. "Pedestrian Detection Based on Hand-crafted Features and Multi-layer Feature Fused-ResNet Model." International Journal on Artificial Intelligence Tools 30, no. 05 (August 2021): 2150028. http://dx.doi.org/10.1142/s0218213021500287.

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One of the most sought-after research areas in object detection is pedestrian detection owing to its applications especially in automated surveillance and robotics. Traditional methods use hand-crafted features to characterize pedestrians. In this work, we have pro-posed a new hand-crafted feature extraction method that concatenates shape, color and texture features; which is then classified by using Support Vector Machine (SVM). As in recent years, deep learning models such as Convolutional Neural Networks (CNNs) have become an eminent state of the art in detection challenges, which unlike the manually designed feature extraction mechanism, results in more accuracy. Therefore, we have also proposed a CNN network, a modification of the pre-trained ResNet-18 named as Multi-layer Feature Fused-ResNet (MF2-ResNet). We have used the proposed modification for (1) feature extraction; which is then classified by using Support Vector Machine (SVM); (2) End-to-End feature extraction and classification by the CNN network and (3) MF2-ResNet based Faster-RCNN to include region proposals in the detection pipeline. To evaluate the proposed method, it is compared with existing pre-trained CNNs. The MF2-ResNet based Faster R-CNN is compared with state-of-the-art detection methods. Three benchmark pedestrian datasets are used in this work: INRIA, NICTA and Daimler.
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Stevens, Robert, Chris Wroe, Sean Bechhofer, Phillip Lord, Alan Rector, and Carole Goble. "Building Ontologies in DAML + OIL." Comparative and Functional Genomics 4, no. 1 (2003): 133–41. http://dx.doi.org/10.1002/cfg.233.

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In this article we describe an approach to representing and building ontologies advocated by the Bioinformatics and Medical Informatics groups at the University of Manchester. The hand-crafting of ontologies offers an easy and rapid avenue to delivering ontologies. Experience has shown that such approaches are unsustainable. Description logic approaches have been shown to offer computational support for building sound, complete and logically consistent ontologies. A new knowledge representation language, DAML + OIL, offers a new standard that is able to support many styles of ontology, from hand-crafted to full logic-based descriptions with reasoning support. We describe this language, the OilEd editing tool, reasoning support and a strategy for the language’s use. We finish with a current example, in the Gene Ontology Next Generation (GONG) project, that uses DAML + OIL as the basis for moving the Gene Ontology from its current hand-crafted, form to one that uses logical descriptions of a concept’s properties to deliver a more complete version of the ontology.
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Chen, L., F. Rottensteiner, and C. Heipke. "DEEP LEARNING BASED FEATURE MATCHING AND ITS APPLICATION IN IMAGE ORIENTATION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (August 3, 2020): 25–33. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-25-2020.

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Abstract. Matching images containing large viewpoint and viewing direction changes, resulting in large perspective differences, still is a very challenging problem. Affine shape estimation, orientation assignment and feature description algorithms based on detected hand crafted features have shown to be error prone. In this paper, affine shape estimation, orientation assignment and description of local features is achieved through deep learning. Those three modules are trained based on loss functions optimizing the matching performance of input patch pairs. The trained descriptors are first evaluated on the Brown dataset (Brown et al., 2011), a standard descriptor performance benchmark. The whole pipeline is then tested on images of small blocks acquired with an aerial penta camera, to compute image orientation. The results show that learned features perform significantly better than alternatives based on hand crafted features.
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Frater, Judy, and Jana M. Hawley. "A hand-crafted slow revolution: Co-designing a new genre in the luxury world." Fashion, Style & Popular Culture 5, no. 3 (October 1, 2018): 299–311. http://dx.doi.org/10.1386/fspc.5.3.299_1.

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Sulaiman, Alaa, Khairuddin Omar, Mohammad F. Nasrudin, and Anas Arram. "Length Independent Writer Identification Based on the Fusion of Deep and Hand-Crafted Descriptors." IEEE Access 7 (2019): 91772–84. http://dx.doi.org/10.1109/access.2019.2927286.

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Zhang, Yi-Xiang, Hong-Bo Zhang, Ji-Xiang Du, Qing Lei, Lijie Yang, and Bineng Zhong. "RGB+2D skeleton: local hand-crafted and 3D convolution feature coding for action recognition." Signal, Image and Video Processing 15, no. 7 (February 23, 2021): 1379–86. http://dx.doi.org/10.1007/s11760-021-01868-8.

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40

Voloshynovskiy, Slava, Olga Taran, Mouad Kondah, Taras Holotyak, and Danilo Rezende. "Variational Information Bottleneck for Semi-Supervised Classification." Entropy 22, no. 9 (August 27, 2020): 943. http://dx.doi.org/10.3390/e22090943.

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In this paper, we consider an information bottleneck (IB) framework for semi-supervised classification with several families of priors on latent space representation. We apply a variational decomposition of mutual information terms of IB. Using this decomposition we perform an analysis of several regularizers and practically demonstrate an impact of different components of variational model on the classification accuracy. We propose a new formulation of semi-supervised IB with hand crafted and learnable priors and link it to the previous methods such as semi-supervised versions of VAE (M1 + M2), AAE, CatGAN, etc. We show that the resulting model allows better understand the role of various previously proposed regularizers in semi-supervised classification task in the light of IB framework. The proposed IB semi-supervised model with hand-crafted and learnable priors is experimentally validated on MNIST under different amount of labeled data.
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Bachhofner, Stefan, Ana-Maria Loghin, Johannes Otepka, Norbert Pfeifer, Michael Hornacek, Andrea Siposova, Niklas Schmidinger, et al. "Generalized Sparse Convolutional Neural Networks for Semantic Segmentation of Point Clouds Derived from Tri-Stereo Satellite Imagery." Remote Sensing 12, no. 8 (April 18, 2020): 1289. http://dx.doi.org/10.3390/rs12081289.

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We studied the applicability of point clouds derived from tri-stereo satellite imagery for semantic segmentation for generalized sparse convolutional neural networks by the example of an Austrian study area. We examined, in particular, if the distorted geometric information, in addition to color, influences the performance of segmenting clutter, roads, buildings, trees, and vehicles. In this regard, we trained a fully convolutional neural network that uses generalized sparse convolution one time solely on 3D geometric information (i.e., 3D point cloud derived by dense image matching), and twice on 3D geometric as well as color information. In the first experiment, we did not use class weights, whereas in the second we did. We compared the results with a fully convolutional neural network that was trained on a 2D orthophoto, and a decision tree that was once trained on hand-crafted 3D geometric features, and once trained on hand-crafted 3D geometric as well as color features. The decision tree using hand-crafted features has been successfully applied to aerial laser scanning data in the literature. Hence, we compared our main interest of study, a representation learning technique, with another representation learning technique, and a non-representation learning technique. Our study area is located in Waldviertel, a region in Lower Austria. The territory is a hilly region covered mainly by forests, agriculture, and grasslands. Our classes of interest are heavily unbalanced. However, we did not use any data augmentation techniques to counter overfitting. For our study area, we reported that geometric and color information only improves the performance of the Generalized Sparse Convolutional Neural Network (GSCNN) on the dominant class, which leads to a higher overall performance in our case. We also found that training the network with median class weighting partially reverts the effects of adding color. The network also started to learn the classes with lower occurrences. The fully convolutional neural network that was trained on the 2D orthophoto generally outperforms the other two with a kappa score of over 90% and an average per class accuracy of 61%. However, the decision tree trained on colors and hand-crafted geometric features has a 2% higher accuracy for roads.
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TRUONG HOANG, Vinh, Duc Phan Van Hoai, Thongchai Surinwarangkoon, Huu-Thanh Duong, and Kittikhun Meethongjan. "A comparative study of rice variety classification based on deep learning and hand-crafted features." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 14, no. 1 (March 23, 2020): 1–10. http://dx.doi.org/10.37936/ecti-cit.2020141.204170.

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Rice is vital to people all around the world. The demand for an efficient method in rice seed variety classification is one of the most essential tasks for quality inspection. Currently, this task is done by technicians based on experience by investigating the similarity of colour, shape and texture of rice. Therefore, we propose to find an appropriate process to develop an automation system for rice recognition. In this paper, several hand-crafted descriptors and Convolutional Neural Networks (CNN) methods are evaluated and compared. The experiment is simulated on the VNRICE dataset on which our method shows a significant result. The highest accuracy obtained is 99.04% by using DenNet21 framework.
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Wang, Shuang, Yonghong Hou, Zhaoyang Li, Jiarong Dong, and Chang Tang. "Combining ConvNets with hand-crafted features for action recognition based on an HMM-SVM classifier." Multimedia Tools and Applications 77, no. 15 (November 3, 2017): 18983–98. http://dx.doi.org/10.1007/s11042-017-5335-0.

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Nijhawan, Rahul, Josodhir Das, and Balasubramanian Raman. "A hybrid of deep learning and hand-crafted features based approach for snow cover mapping." International Journal of Remote Sensing 40, no. 2 (September 20, 2018): 759–73. http://dx.doi.org/10.1080/01431161.2018.1519277.

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Connell, Kate. "From Hand‐Crafted to Electronic: Designing a Digital Mural that Brings an Archive to Life." Library Hi Tech News 22, no. 3 (March 2005): 10–14. http://dx.doi.org/10.1108/07419050510601551.

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Wang, Jingan, Kangjun Shi, Lei Wang, Zhengxin Li, Fengxin Sun, Ruru Pan, and Weidong Gao. "Fusing Convolutional Neural Network Features With Hand-Crafted Features for Objective Fabric Smoothness Appearance Assessment." IEEE Access 8 (2020): 110678–92. http://dx.doi.org/10.1109/access.2020.3001354.

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Ben-David, Arie, and Eibe Frank. "Accuracy of machine learning models versus “hand crafted” expert systems – A credit scoring case study." Expert Systems with Applications 36, no. 3 (April 2009): 5264–71. http://dx.doi.org/10.1016/j.eswa.2008.06.071.

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Viswanatha Reddy, G., C. V. R. Dharma Savarni, and Snehasis Mukherjee. "Facial expression recognition in the wild, by fusion of deep learnt and hand-crafted features." Cognitive Systems Research 62 (August 2020): 23–34. http://dx.doi.org/10.1016/j.cogsys.2020.03.002.

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LIU, GUOYANG, XIAO HAN, LAN TIAN, WEIDONG ZHOU, and HUI LIU. "ECG quality assessment based on hand-crafted statistics and deep-learned S-transform spectrogram features." Computer Methods and Programs in Biomedicine 208 (September 2021): 106269. http://dx.doi.org/10.1016/j.cmpb.2021.106269.

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Ganter, Granville. "Mistress of Her Art: Anne Laura Clarke, Traveling Lecturer of the 1820s." New England Quarterly 87, no. 4 (December 2014): 709–46. http://dx.doi.org/10.1162/tneq_a_00418.

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This essay examines Anne Laura Clarke, a public lecturer from 1822 through the mid-1830s. Her topics ranged from western history to world clothing customs, and she employed hand-crafted historical charts and magic lantern images. The essay is a contribution to feminist history and recovers Clarke's manuscript lectures and visual materials.
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