H. B. Kekre

Bio: H. B. Kekre is an academic researcher. The author has contributed to research in topics: Wavelet transform & Discrete wavelet transform. The author has an hindex of 19, co-authored 163 publications receiving 1382 citations.

Image retrieval using color-texture features from dct on vq code vectors obtained by kekres fast codebook generation

Abstract: novel technique for image retrieval using the color- texture features extracted from images based on vector quantization with Kekre's fast codebook generation is proposed. This gives better discrimination capability for Content Based Image Retrieval (CBIR). Here the database image is divided into 2x2 pixel windows to obtain 12 color descriptors per window (Red, Green and Blue per pixel) to form a vector. Collection of all such vectors is a training set. Then the Kekre's Fast Codebook Generation (KFCG) is applied on this set to get 16 codevectors. The Discrete Cosine Transform (DCT) is applied on these codevectors by converting them to column vector. This transform vector is used as the image signature (feature vector) for image retrieval. The method takes lesser computations as compared to conventional DCT applied on complete image. The method gives the color-texture features of the image database at reduced feature set size. Proposed method avoids resizing of images which is required for any transform based feature extraction method.

Increased Capacity of Information Hiding in LSB-s Method for Text and Image

Abstract: — Steganography, derived from Greek, literally means “covered writing”. It includes a vast array of secret communications methods that conceal the message’s very existence. These methods include invisible inks, microdots, character arrangement, digital signatures, covert channels, and spread spectrum communications. This paper proposes a new improved version of Least Significant Bit (LSB) method. The approach proposed is simple for implementation when compared to Pixel value Differencing (PVD) method and yet achieves a High embedding capacity and imperceptibility. The proposed method can also be applied to 24 bit color images and achieve embedding capacity much higher than PVD. Keywords — Information Hiding, LSB Matching, PVD Steganography. I. I NTRODUCTION NFORMATION hiding techniques have been receiving much attention today. The main motivation for this is largely due to fear of encryption services getting outlawed [14], and copyright owners who want to track confidential and intellectual property copyright against unauthorized access and use in digital materials such as music, film, book and software through the use of digital watermarks. Encryption and Decryption algorithms are widely used to encrypt secret (confidential) data so that it is not directly accessible to the otherwise illegitimate person and whenever the owner or genuine person requires the data, it can be decrypted with the help of a key or with the help of a retrieving algorithm/function. Steganography has a different approach to deal with this problem. Steganography [15] is an application of information hiding. Steganography or Stego as it is often referred to in the IT community, literally means, "covered writing" which is derived from the Greek language. Steganography is defined by Markus Kahn [3] as follows, "Steganography is the art and science of communicating in a way which hides the existence of the communication. In contrast to Cryptography, where the enemy is allowed to detect, intercept and modify messages without being able to violate certain security premises guaranteed by a

Image Segmentation using Extended Edge Operator for Mammographic Images

Abstract: Detection of edges in an image is a very important step towards understanding image features. Since edges often occur at image locations representing object boundaries, edge detection is extensively used in image segmentation when images are divided into areas corresponding to different objects. This can be used specifically for enhancing the tumor area in mammographic images. In this paper extended Sobel , Prewitt and Kirsch edge operators are proposed for image segmentation of mammographic images. Edges and tumor location can be seen clearly by using this method. For comparison purpose Gray level co-occurrence matrix, watershed algorithm, present Sobel, Prewitt and Kirsch edge operators are used and their results are displayed. Diagnostic imaging is an invaluable tool in medicine today. These imaging modalities provide an effective means for noninvasive mapping of the anatomy of a subject. These technologies have greatly increased knowledge of normal and diseased anatomy for medical research and are a critical component in diagnosis and treatment planning. With the increasing size and number of medical images, the use of computers in facilitating their processing and analysis has become necessary. Estimation of the volume of the whole organ, parts of the organ and/or objects within an organ i.e. tumors is clinically important in the analysis of medical image. The relative change in size, shape and the spatial relationships between anatomical structures obtained from intensity distributions provide important information in clinical diagnosis for monitoring disease progression. Therefore, radiologists are particularly interested to observe the size, shape and texture of the organs and/or parts of the organ. For this, organ and tissue morphometry performed in every radiological imaging centre. These routine assessments are commonly subjective and quantitative, and reports typically refer to lesions as large, small, and prominent. The clinical reports usually offer morphometric data in terms of change relative to a prior study. The recognition, labeling and the quantitative measurement of specific objects and structures are involved in the analysis of medical images. Therefore, to provide the information about an object clinically in terms of its size and shape, image segmentation and classification are important tools needed to give the desired information. Medical images edge detection is an important work for object recognition of the human organs such as lungs and ribs, and it is an essential pre-processing step in medical image

Off-Line Signature Recognition Systems

Abstract: Handwritten signature is one of the most widely used biometric traits for authentication of person as well as document. In this paper we discuss issues regarding off-line signature recognitions. We review existing techniques, their performance and method for feature extraction. We discuss a system designed using cluster based global features which is a multi algorithmic offline signature recognition system.

Color Appearance Models

Abstract: Color science is a multidisciplinary field with broad applications in industries such as digital imaging, coatings and textiles, food, lighting, archiving, art, and fashion. Accurate definition and measurement of color appearance is a challenging task that directly affects color reproduction in such applications. Color Appearance Models addresses those challenges and offers insight into the preferred solutions. Extensive research on the human visual system (HVS) and color vision has been performed in the last century, and this book contains a good overview of the most important and relevant literature regarding color appearance models.

Estimating CT Image From MRI Data Using Structured Random Forest and Auto-Context Model

Abstract: Computed tomography (CT) imaging is an essential tool in various clinical diagnoses and radiotherapy treatment planning. Since CT image intensities are directly related to positron emission tomography (PET) attenuation coefficients, they are indispensable for attenuation correction (AC) of the PET images. However, due to the relatively high dose of radiation exposure in CT scan, it is advised to limit the acquisition of CT images. In addition, in the new PET and magnetic resonance (MR) imaging scanner, only MR images are available, which are unfortunately not directly applicable to AC. These issues greatly motivate the development of methods for reliable estimate of CT image from its corresponding MR image of the same subject. In this paper, we propose a learning-based method to tackle this challenging problem. Specifically, we first partition a given MR image into a set of patches. Then, for each patch, we use the structured random forest to directly predict a CT patch as a structured output, where a new ensemble model is also used to ensure the robust prediction. Image features are innovatively crafted to achieve multi-level sensitivity, with spatial information integrated through only rigid-body alignment to help avoiding the error-prone inter-subject deformable registration. Moreover, we use an auto-context model to iteratively refine the prediction. Finally, we combine all of the predicted CT patches to obtain the final prediction for the given MR image. We demonstrate the efficacy of our method on two datasets: human brain and prostate images. Experimental results show that our method can accurately predict CT images in various scenarios, even for the images undergoing large shape variation, and also outperforms two state-of-the-art methods.