Jayanta Mukherjee

Bio: Jayanta Mukherjee is an academic researcher from Indian Institute of Technology Kharagpur. The author has contributed to research in topics: Discrete cosine transform & Euclidean distance. The author has an hindex of 24, co-authored 187 publications receiving 2071 citations. Previous affiliations of Jayanta Mukherjee include Indian Institutes of Technology.

Enhancement of Color Images by Scaling the DCT Coefficients

Abstract: This paper presents a new technique for color enhancement in the compressed domain. The proposed technique is simple but more effective than some of the existing techniques reported earlier. The novelty lies in this case in its treatment of the chromatic components, while previous techniques treated only the luminance component. The results of all previous techniques along with that of the proposed one are compared with respect to those obtained by applying a spatial domain color enhancement technique that appears to provide very good enhancement. The proposed technique, computationally more efficient than the spatial domain based method, is found to provide better enhancement compared to other compressed domain based approaches.

New efficient methods of image compression in digital cameras with color filter array

Abstract: Many consumer digital color cameras use a single light sensitive sensor and a color filter array (CFA) with each pixel element recording intensity information of one color component. The captured data is interpolated into a full color image, which is then compressed in many applications. Carrying out color interpolation before compression introduces redundancy in the data. In this paper we discuss methods and issues involved in the compression of CFA data before full color interpretation. The compression methods described operate on the same number of pixels as the sensor data. To obtain improved image quality, median filtering is applied as post-processing. Furthermore, to assure low complexity, the CFA data is compressed by JPEG. Simulations have demonstrated that substantial improvement in image quality is achievable using these new schemes.

Image resizing in the compressed domain using subband DCT

Abstract: Resizing of digital images is needed in various applications, such as transmission of images over communication channels varying widely in their bandwidths, display at different resolutions depending on the resolution of a display device, etc. In this work, we propose a modification of a recently proposed elegant image resizing algorithm by Dugad and Ahuja (2001). We have also extended their approach and our modified versions to color images and studied their performance at different levels of compression for an image. Our proposed modified algorithms, in general, perform better than the earlier method in most cases. Though there is a marginal increase in the computation required in image-halving, the computation overhead of the proposed modification is higher compared to the Dugad-Ahuja algorithm in the case of doubling the images.

Gait recognition using Pose Kinematics and Pose Energy Image

Abstract: Many of the existing gait recognition approaches represent a gait cycle using a single 2D image called Gait Energy Image (GEI) or its variants. Since these methods suffer from lack of dynamic information, we model a gait cycle using a chain of key poses and extract a novel feature called Pose Energy Image (PEI). PEI is the average image of all the silhouettes in a key pose state of a gait cycle. By increasing the resolution of gait representation, more detailed dynamic information can be captured. However, processing speed and space requirement are higher for PEI than the conventional GEI methods. To overcome this shortcoming, another novel feature named as Pose Kinematics is introduced, which represents the percentage of time spent in each key pose state over a gait cycle. Although the Pose Kinematics based method is fast, its accuracy is not very high. A hierarchical method for combining these two features is, therefore, proposed. At first, Pose Kinematics is applied to select a set of most probable classes. Then, PEI is used on these selected classes to get the final classification. Experimental results on CMU's Mobo and USF's HumanID data set show that the proposed approach outperforms existing approaches.

Design, Development, and Clinical Evaluation of the Electronic Mobility Cane for Vision Rehabilitation

Abstract: This paper proposes a new electronic mobility cane (EMC) for providing obstacle detection and way-finding assistance to the visually impaired people. The main feature of this cane is that it constructs the logical map of the surrounding environment to deduce the priority information. It provides a simplified representation of the surrounding environment without causing any information overload. It conveys this priority information to the subject by using intuitive vibration, audio or voice feedback. The other novel features of the EMC are staircase detection and nonformal distance scaling scheme. It also provides information about the floor status. It consists of a low power embedded system with ultrasonic sensors and safety indicators. The EMC was subjected to series of clinical evaluations in order to verify its design and to assess its ability to assist the subjects in their daily-life mobility. Clinical evaluations were performed with 16 totally blind and four low vision subjects. All subjects walked controlled and the real-world test environments with the EMC and the traditional white cane. The evaluation results and significant scores of subjective measurements have shown the usefulness of the EMC in vision rehabilitation services.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Data clustering: a review

Abstract: 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.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Medscape

Abstract: Secret History: Return of the Black Death Channel 4, 7-8pm In 1348 the Black Death swept through London, killing people within days of the appearance of their first symptoms. Exactly how many died, and why, has long been a mystery. This Secret History documentary follows experts as they pick through the evidence and reveal why the plague killed on such a scale. And they ask, what might be coming next?

Fast Tree: Computing Large Minimum-Evolution Trees with Profiles instead of a Distance Matrix

Abstract: Gene families are growing rapidly, but standard methods for inferring phylogenies do not scale to alignments with over 10,000 sequences. We present FastTree, a method for constructing large phylogenies and for estimating their reliability. Instead of storing a distance matrix, FastTree stores sequence profiles of internal nodes in the tree. FastTree uses these profiles to implement neighbor-joining and uses heuristics to quickly identify candidate joins. FastTree then uses nearest-neighbor interchanges to reduce the length of the tree. For an alignment with N sequences, L sites, and a different characters, a distance matrix requires O(N^2) space and O(N^2 L) time, but FastTree requires just O( NLa + N sqrt(N) ) memory and O( N sqrt(N) log(N) L a ) time. To estimate the tree's reliability, FastTree uses local bootstrapping, which gives another 100-fold speedup over a distance matrix. For example, FastTree computed a tree and support values for 158,022 distinct 16S ribosomal RNAs in 17 hours and 2.4 gigabytes of memory. Just computing pairwise Jukes-Cantor distances and storing them, without inferring a tree or bootstrapping, would require 17 hours and 50 gigabytes of memory. In simulations, FastTree was slightly more accurate than neighbor joining, BIONJ, or FastME; on genuine alignments, FastTree's topologies had higher likelihoods. FastTree is available at http://microbesonline.org/fasttree.