Hrushikesh Garud

Brightness preserving dynamic fuzzy histogram equalization

This paper proposes a novel modification of the brightness preserving dynamic histogram equalization technique to improve its brightness preserving and contrast enhancement abilities while reducing its computational complexity. The modified technique, called Brightness Preserving Dynamic Fuzzy Histogram Equalization (BPDFHE), uses fuzzy statistics of digital images for their representation and processing. Representation and processing of images in the fuzzy domain enables the technique to handle the inexactness of gray level values in a better way, resulting in improved performance. Execution time is dependent on image size and nature of the histogram, however experimental results show it to be faster as compared to the techniques compared here. The performance analysis of the BPDFHE along with that for BPDHE has been given for comparative evaluation.

Brightness preserving contrast enhancement in digital pathology

Majority of images in digital pathology require post acquisition adjustments to optimize brightness, contrast, and image visibility. Despite this fact much research effort has not been put in development of customized contrast enhancement techniques for this application. In this respect various contrast enhancement techniques developed for other application areas can be customized for the digital pathology. This paper proposes use of a brightness preserving contrast enhancement technique for image enhancement in digital pathology applications and compares its functional superiority over other contemporary techniques.

Volume visualization approach for depth-of-field extension in digital pathology

Microscope optics presents two critical limitations of low depth of field and very low or no depth perception. Due to low depth of field, at high magnification levels microscope cannot adequately focus on the object. This can adversely affect the performance of slide digitization procedure in digital pathology. This paper presents a depth of field extension and 3D visualization technique for pathological applications. The technique uses a sequence of low depth of field images of object for generation of high resolution extended depth of field view of the scene by volume visualization technique such as ray casting. This work is directed towards incorporating the method in digital pathology work-flow. Sensing and computational tasks (image sequence acquisition, focus measure computation and volume creation, and volume rendering) involved in the method can be executed by different processing units available in modern computational platforms to achieve high degree of parallelism in implementation and accomplish real time image generation and visualization.

High throughput VLSI architecture for HEVC SAO encoding for ultra HDTV

This paper presents a high performance, silicon area efficient, and software configurable hardware architecture for sample adaptive offset (SAO) encoding. The paper proposes a novel architecture consisting of single largest coding unit (LCU) stage SAO operation, unified data path for luma and chroma channels, add-on external interfaces on frame level statistics collection units to allow fine control over the parameter estimation process, flexible rate control and artifact avoidance algorithms. The unified data path consists of 2D-block based processing with 3 pipeline stages for statistics generation and multiple offset rate-distortion cost estimation blocks for high performance. The proposed design after placement and routing is expected to take-up approximately 0.15 mm2 of silicon area in 28nm CMOS process. The proposed design at 200 MHz supports 4K Ultra HD video encoding at 60fps. Simulation experiments have shown average bit-rate saving of up to 4.3% with in-loop SAO filtering and various encoder configurations.

High Performance Front Camera ADAS Applications on TI's TDA3X Platform

Advanced driver assistance systems (ADAS) are designed to increase drivers situational awareness and road safety by providing essential information, warning and automatic intervention to reduce the possibility/severity of an accident. Of the various types of ADAS modalities available, camera based ADAS are being widely adopted for their usefulness in varied applications, overall reliability and adaptability to new requirements. But camera based ADAS also represents a complex, high-performance, and low-power compute problem, requiring specialized solutions. This paper introduces a high performance front camera ADAS based on a small area, low power System-on-Chip (SoC) solution from Texas Instruments called Texas Instruments Driver Assist 3x (TDA3x). The paper illustrates compute capabilities of the device in implementation of a typical front camera ADAS. The paper also introduces key programming concepts related to heterogeneous programmable compute cores in the SoC and the software framework to use those cores in order to develop the front camera solutions. These aspects will be of interest not only to the ADAS developers but for computer vision and compute intensive embedded system development.

A fast auto white balance scheme for digital pathology

This paper presents a fast and accurate auto white balance scheme specifically suited for white balance (WB) correction in digital pathology applications. The scheme uses low-level statistics based technique for illuminant estimation and linear transformation method for image correction. The WB performance of the proposed scheme has been tested in various real life conditions and found to be within just noticeable difference of the ideal WB scenario.

Breast fine needle aspiration cytology practices and commonly perceived diagnostic significance of cytological features: A pan- India survey

Background: Methodical and meticulous understanding of clinico-pathological procedures and decision making process of cancer diagnosis and identification of aspects that are well-suited for computer-aided analysis are first steps toward development of assistive computational tool for analysis of breast fine needle aspiration cytology (FNAC) slides. Aims: To identify variables in practice of FNAC as used for diagnosis of breast lesions and commonly perceived diagnostic significance of cytological features for diagnosis of benign or malignant condition of breast lesions. Materials and Methods: An India-wide questionnaire-based survey of cytopathologists/pathologists’ breast FNAC reporting practices and their opinion on diagnostic significance of cytological features in diagnosis of benign or malignant nature of breast lesion were conducted. Results: Fifty-one experts working with various medical education institutes (~52% of participants), oncological tertiary care centers (~28%) and primary care centers/private diagnostic pathology laboratories (~20%) spread over 13 states of India have participated in the survey. Constants and variables observed in clinico-cytopathological practices and combined opinion of the participants on diagnostic significance of cytological features are presented here. Conclusions: There exist analogous as well as varied components in clinico-pathological procedures and diagnostic interpretation by individuals. These constants and variables in the practice of breast FNAC should be considered, when drawing up specifications for an assistive computational tool for analysis of breast FNAC slides. The estimate for commonly perceived significance of cytological features obtained through this study will help in their selection for computer-aided analysis of breast FNAC slides and further in selection of corresponding feature quantification techniques.

A Diverse Low Cost High Performance Platform for Advanced Driver Assistance System (ADAS) Applications

Advanced driver assistance systems (ADAS) are becoming more and more popular. Lot of the ADAS applications such as Lane departure warning (LDW), Forward Collision Warning (FCW), Automatic Cruise Control (ACC), Auto Emergency Braking (AEB), Surround View (SV) that were present only in high-end cars in the past have trickled down to the low and mid end vehicles. Lot of these applications are also mandated by safety authorities such as EUNCAP and NHTSA. In order to make these applications affordable in the low and mid end vehicles, it is important to have a cost effective, yet high performance and low power solution. Texas Instruments (TIs) TDA3x is an ideal platform which addresses these needs. This paper illustrates mapping of different algorithms such as SV, LDW, Object detection (OD), Structure From Motion (SFM) and Camera-Monitor Systems (CMS) to the TDA3x device, thereby demonstrating its compute capabilities. We also share the performance for these embedded vision applications, showing that TDA3x is an excellent high performance device for ADAS applications.

A fast color constancy scheme for automobile video cameras

Color constancy (CC) is an important requirement for all digital imaging and many computer vision systems. Video photography applications such as automobile video cameras required to have the CC schemes that exhibit quick and accurate response to rapidly changing scene illuminant. This paper presents a fast and effective CC scheme specifically suited for automobile video cameras. The proposed CC scheme uses combination of a static method for illuminant estimation called White Patch Retinex (WPR) and a computationally efficient linear transformation model for WB correction of the images. The scheme also introduces novel temporal filtering of the WB parameters to avoid the field flicker noise. Exhaustive testing of the scheme in laboratory and real life test conditions have shown the it to be effective under various lighting conditions. The paper also presents details of its implementation on an embedded processing platform to achieve HD video processing at the frame rate of 60 frames per second.

Learning scale-space representation of nucleus for accurate localization and segmentation of epithelial squamous nuclei in cervical smears

Computer vision systems are being introduced in pre-screening of cervical cytopathology slides to identify samples that require study by cytopathologists. These systems work on the principle of imaging and analysis of cytology features in general and nuclear features in particular. Thus accurate localization and segmentation of the nuclei is crucial for the systems. Though several methods have been conceptualized, developed and employed to achieve the tasks of localization and segmentation of nuclei in cytology images, most fail to localize nuclei with opened up chromatin. This paper presents a machine learning approach based framework for accurate localization and segmentation of nuclei. The approach uses the random forest model to learn complete scale-space representation of the nuclear chromatin distribution in green and color saturation channels. Based on the multi scale features of an unknown image this model can predict an image such that gray level value of a pixel is proportionate to the probability that the pixel belongs to nuclear region. This predicted image then can be used for accurate localization and segmentation of the nuclei by random walks approach. Accuracy of the system has been tested on a publicly available dataset images and was found to be approximately 97%.