Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.

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Data clustering: a review

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten notes in a PDA, in postal addresses on envelopes, in amounts in bank checks, in handwritten fields in forms, etc. This overview describes the nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms. Both the online case (which pertains to the availability of trajectory data during writing) and the off-line case (which pertains to scanned images) are considered. Algorithms for preprocessing, character and word recognition, and performance with practical systems are indicated. Other fields of application, like signature verification, writer authentification, handwriting learning tools are also considered.

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Online and off-line handwriting recognition: a comprehensive survey

IEEE Transactions on Pattern Analysis and Machine Intelligence

The deep learning models, which include attention mechanisms, are shown to enhance the performance and efficiency of the various computer vision tasks such as pattern recognition, object detection, face recognition, etc. Although the visual attention mechanism is the source of inspiration for these models, recent attention models consider ’attention’ as a pure machine vision optimization problem, and visual attention remains the most neglected aspect. Therefore, this paper presents a collaborative human and machine attention module which considers both visual and network’s attention. The proposed module is inspired by the dorsal (‘where’) pathways of visual processing and can be integrated with any convolutional neural network (CNN) model. First, the module computes the spatial attention map from the input feature maps, which is then combined with the visual attention maps. The visual attention maps are created using eye-fixations obtained by performing an eye-tracking experiment with human participants. The visual attention map covers the highly salient and discriminating image regions as humans tend to focus on such regions, whereas the other relevant image regions are processed by spatial attention map. The combination of these two maps results in the finer refinement in feature maps, resulting in improved performance. The comparative analysis reveals that our model not only shows significant improvement over the baseline model but also outperforms the other models. We hope that our findings using a collaborative human-machine attention module will be helpful in other computer vision tasks as well.

Collaborative Human Machine Attention Module for Character Recognition

Skeletal Bone age assessment is a routine clinical procedure carried out by paediatricians and endocrinologists for investigating a variety of endocrinological, metabolic, genetic and growth disorders in children. Skeletal maturity advances with change in structure and size of the skeletal bones with respect to age. This is commonly done by radiological investigation of the left hand due to its non dominant use. Dissent in the skeletal age and bone age values indicates abnormality. In this study, a bone-age assessment model using triplet loss for children in 0–3 years of age is proposed. Furthermore, this is the first automated bone age assessment study on lower age groups with comparable results, using one tenth of the training data samples as opposed to conventional deep neural networks. We have used small number of radiographs per class from Digital Hand Atlas Database System (DHA), a publicly available comprehensive x-ray dataset. Model trained achieves an AUC of 0.92 for binary and 0.82 for multi-class classification with visible separation in embedding clusters; thereby resulting in correct predictions on test data set.

Bone Age Assessment for Lower Age Groups Using Triplet Network in Small Dataset of Hand X-Rays

We propose an empirical computational model for generating an interpretation of a video shot based on our proposed principle of perceptual prominence. The principle of perceptual prominence captures the key aspects of mise-en-scene required for interpreting a video scene. We present a novel approach for applying perceptual grouping principles to the spatio-temporal domain of video. Our spatio-temporal perceptual grouping scheme, applied on blob tracks, makes use of a specified spatio-temporal coherence model. A high level semantic interpretation of scenes is done using the mise-en-scene features and the perceptual prominence computed for the perceptual clusters.

Video Scene Interpretation Using Perceptual Prominence and Mise-en-scène Features

Human gaze dynamics mainly concern about the sequence of the occurrence of three eye movements: fixations, saccades, and microsaccades. In this paper, we correlate them as three different states to velocities of eye movements. We build a state trajectory estimator based on ancestor sampling (ST EAS) model, which captures the features of the human temporal gaze pattern to identify the kind of visual stimuli. We used a gaze dataset of 72 viewers watching 60 video clips which are equally split into four visual categories. Uniformly sampled velocity vectors from the training set, are used to find the best suitable parameters of the proposed statistical model. Then, the optimized model is used for both gaze data classification and video retrieval on the test set. We observed 93.265% of classification accuracy and a mean reciprocal rank of 0.888 for video retrieval on the test set. Hence, this model can be used for viewer independent video indexing for providing viewers an easier way to navigate through the contents.

Eye Movement State Trajectory Estimator based on Ancestor Sampling

This paper addresses the challenges in creating good quality composite 3D contents for 3DTV applications and post-production visual-effects. We present a novel content-aware compositing technique that faithfully preserves the salient structures of cloned source and target content, and avoid major conflicting stereopsis cues to maintain a pleasant 3D illusion altogether. Our approach typically learns the appearance layouts of both source and target scene elements. The system extracts object's significance prior maps using classified labels and derive geometric transforms to compensate the 3D perspective mismatches between source and target images using a novel depth image-based rendering procedure. For seamless cloning, we apply a new depth-consistent interpolant technique which utilizes the classified likelihood confidences in weighting the salient or low-significant regions and re-estimating the plausible depth values of the cloned region in accordance with target 3D structure. Further, we adopt a novel content-preserving local warping scheme to reduce the apparent distortions in object shape, size and perspective. Finally, we propose a content-aware mean value cloning technique that seamlessly merges the warped cloned patches with the geometric-appearance context of new background and homogenize vague boundaries with the aid of an object salient map to remove the smudging effects. The overall process is formulated as an energy minimization problem and optimally regularized for large warps, vertical disparities, and stereo baseline changes. Plausible results are demonstrated to show the effectiveness of our approach.

Santanu Chaudhury

Papers

Collaborative Human Machine Attention Module for Character Recognition

Bone Age Assessment for Lower Age Groups Using Triplet Network in Small Dataset of Hand X-Rays

Video Scene Interpretation Using Perceptual Prominence and Mise-en-scène Features

Eye Movement State Trajectory Estimator based on Ancestor Sampling

Content-aware seamless stereoscopic 3D compositing