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.

/pdf/data-clustering-a-review-4c452f5cab.pdf

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.

/pdf/online-and-off-line-handwriting-recognition-a-comprehensive-4qmt9ty596.pdf

Online and off-line handwriting recognition: a comprehensive survey

IEEE Transactions on Pattern Analysis and Machine Intelligence

High-throughput identification of digital traits encapsulating the changes in plant’s internal structure under drought stress, based on hyperspectral imaging (HSI) is a challenging task. This is due to the high spectral and spatial resolution of HSI data and lack of labelled data. Therefore, this work proposes a novel framework for phenotypic discovery based on autoencoders, which is trained using Simple Linear Iterative Clustering (SLIC) superpixels. The distinctive archetypes from the learnt digital traits are selected using simplex volume maximisation (SiVM). Their accumulation maps are employed to reveal differential drought responses of wheat cultivars based on t-distributed stochastic neighbour embedding (t-SNE) and the separability is quantified using cluster silhouette index. Unlike prior methods using raw pixels or feature vectors computed by fusing predefined indices as phenotypic traits, our proposed framework shows potential by separating the plant responses into three classes with a finer granularity. This capability shows the potential of our framework for the discovery of data-driven phenotypes to quantify drought stress responses.

Extraction of Phenotypic Traits for Drought Stress Study Using Hyperspectral Images

A connectionist clustering strategy is presented for segmenting color images. First the local peaks in the 3-D red, green, blue histogram are located. Then using these as the prototypes other patterns are classified into one of them. The prototype selection method employs only neuronal dynamics and therefore is faster than existing clustering neural networks. The classification network takes into account the distribution of the data and hence is less prone to misclassifications. Experimental results obtained by applying the network for segmenting one color image are presented. >

A connectionist approach for color image segmentation

Conceptual organization of semi-structured documents can help in effective retrieval from collections of emails, product complaints, video descriptions etc. In this paper, we propose a conceptual organization scheme for grouping and categorizing semi-structured text data using natural language processing techniques. We propose a knowledge-discovery mechanism that extracts noun phrases from documents and arranges them into concept maps based on their co-occurrence. The emerging concept maps can be used for automatic grouping and conceptual categorization of documents. Further, Phrase structure Grammar is employed to extract relationships among these entities from documents and index the document collection with these relations.

Knowledge Discovery from Semi-Structured Data for Conceptual Organization

Effective self-organization schemes lead to the creation of autonomous and reliable robot teams that can outperform a single, sophisticated robot on several tasks. We present here a novel, vision-based microscopic framework for active and distributed object-recognition and pose-estimation using a team of robots of simple construction. The team performs the task of locating a given object(s) in an unknown territory, recognizing it with sufficient confidence and estimating its pose. The larger goal is to experiment with probabilistic frameworks and graph-theoretic methods in the design of robot teams to achieve autonomous self-organization independent of the task at hand. We have chosen 3D object recognition as a first problem area to evaluate the effectiveness of our system design. The system comprises a probabilistic framework for the successful detection of the object in a coordinated manner and adaptive measures in case of machinery failures or presence of obstacles. A pose estimation method for the detected object and graph theoretic solutions for optimal field coverage by the robots are also presented. Each robot is provided with a part-based, spatial model of the object. The object to be recognized is taken to be much bigger than the robots and need not fit completely into the field of view of the robot cameras. We assume no knowledge of the internal parameters of the robot cameras and perform no camera calibration procedures. Initial simulation results corroborate our system design and field coverage methods.

A Microscopic Framework For Distributed Object-Recognition & Pose-Estimation

Disasters themselves are not limited to specific parts of world, though certain areas might be more prone to certain specific types of disasters. Some countries are more prone to terrorist activities, some coastal areas are more prone to cyclones, some areas are more prone to floods while some other areas are prone to oil spills. Loss of human life and property are obvious consequences of disasters. However, the level of preparedness is the key element that can limit the extent of damage. Use of sensor network based technologies can enhance the level of preparedness and the ability to handle consequences of the disaster. This higher level of preparedness can provide a better control over the loss. A team of mobile robots can quickly set up a network of mobile sensors and actuators for rapid action. This talk presents an overview of applications of distributed mobile robots in disaster management.Applications which have human risks such as handling of nuclear waste, identification of location of explosives, etc., show the potential of use of mobile robots functioning as a group. Mobile robots have been used in search and rescue operation of World Trade centre terrorist attack and Hanshin-Awaji earthquake. In such situations mobile robots can enter voids too small or deep for a person, and can begin surveying larger voids that people are not permitted to enter until a fire has been put out or the structure has been reinforced. Robots can carry cameras, thermal imagers, hazardous material detectors, and medical payloads into the interior of a rubble pile and set up communication link with human operator using the ad-hoc network set-up by these robots. Each robot equipped with accelerometer, gyroscope and magnetic compass as sensor devices, can plan its navigational path with reference to each other and can get the sensor network dynamically relocated. Team of mobile robots equipped with appropriate sensors and distributed and cooperative planning algorithms can also autonomously generate maps for oil spill or radiation leaks.In this context obviously the protocol for coalition formation between multiple robots becomes an important issue. Formation Control strategies have been developed focusing on control and coordination for multiple robots that have to move as a group with user-defined relative positions, i.e., in formations for performing different tasks. In case of disaster management, with human in the loop, a new problem, that of coalition formation in a team consisting of multiple robots and human beings, needs to be addressed.

Santanu Chaudhury

Papers

Extraction of Phenotypic Traits for Drought Stress Study Using Hyperspectral Images

A connectionist approach for color image segmentation

Knowledge Discovery from Semi-Structured Data for Conceptual Organization

A Microscopic Framework For Distributed Object-Recognition & Pose-Estimation

Disaster management using mobile robots