Adaptive Progressive Thresholding Research Articles

Development of Adaptive Threshold and Data Smoothening Algorithm for GPR Imaging

There are many approaches available to separate the background and foreground in image processing applications. Currently, researchers are focusing on wavelet De-noising, curvelet threshold, Edge Histogram Descriptor threshold, Otsu thresholding, recursive thresholding and adaptive progressive thresholding. In fixed and predictable background conditions, above techniques separate background and foreground efficiently. In a common scenario, background reference is blind due to soil surface moisture content and its non-linearity. There are many methodologies proposed from time to time by researchers to solve this blind reference background separation. But challenges still now remain, because there are two major problems in ground penetrating radar imaging such as targets like ground enhances the false alarm and non-metallic target detection, where the threshold decision is a critical task. In this paper, a novel real time blind adaptive threshold algorithm is proposed for ground penetrating radar image processing. The blind threshold was decided to use normal random variable variance and image data variance. Further, the image was smoothened by random variance ratio to image data variance. Experimental results showed satisfactory results for the background separation and smoothening the targeted image data with the proposed algorithm.

An automated feature-localisation algorithm for a feature-specific modular approach for face recognition

Novel techniques for accurate location of the eyes and nose of a person in a complex-lighting environment are presented in this paper. An adaptive progressive thresholding technique is applied to spot the darkest regions representing the eyes in a face. The nose region is located by performing cumulative histogram-based thresholding of the gradient image formed below the eye region. A feature-specific modular Principal Component Analysis (PCA) approach on face images is performed with the identified features for face recognition. Principal components are extracted from non-overlapping modules of the image and are concatenated to make a single signature vector to represent the face in a particular viewing angle. Additional principal components are extracted from the key facial features and are added as an extension to the signature vector. The feature-specific modular PCA approach is capable of recognising faces in varying illumination conditions and facial expressions, as the modular components represent the local information of the facial regions.

EFFICIENT ARCHITECTURES FOR SEGMENTATION OF ENDOSCOPIC IMAGES IN MICRO-ROBOTIC AUTO NAVIGATION SYSTEMS

This paper presents novel techniques and their architectural translations for real-time image segmentation of endoscopic images, which are required for micro-robotic auto navigation systems. The proposed technique is based on a two-step process to segment the lumen regions from endoscopic images. In the first step, an adaptive progressive thresholding technique based on Otsu's method is employed to obtain a preliminary region of interest of the lumen region. A novel architecture for the between-class variance computation of Otsu's method is presented to meet the real-time requirements of the system. The proposed implementation employs binary logarithmic computations to eliminate the complex divisions and multiplications in Otsu's procedure. In the second step, an Iris filter is employed to enhance the boundary of the region of interest to facilitate an accurate detection of the lumen region. An architecture based on the coordinate rotation digital computer is proposed to simplify the complex computations of trigonometric functions required by the Iris filter operation. Software simulations demonstrate that the proposed technique requires a significantly smaller number of iterations to obtain an accurate segmentation result as compared to a previously reported method. In addition, synthesis results on the field-programmable gate array show that the proposed architectures can achieve high performance with low hardware resource utilization.

Area–time efficient between-class variance module for adaptive segmentation process

Adaptive progressive thresholding (APT) has been shown to be an efficient method to segment the lumen region of endoscopic images. A pipelined architecture was previously proposed in an attempt to accelerate the conventional APT in hardware. In the paper, a novel architecture for the between-class variance computations of APT is presented to minimise the severe bottleneck of the iterative loop in the APT process. The technique employs binary logarithm conversion to eliminate the computationally intensive dividers and reduce the complexity of the multipliers of the previous architecture. The proposed method employs a re-configurable logarithmic computing unit, which can be configured to achieve a highly accurate between-class variance unit. It has been shown that the proposed approach leads to area–time efficient FPGA implementation which is capable of a computation speed-up of ∼2.75 times while occupying only one-sixth of the number of slices required by the previous approach.

Segmenting endoscopic images using adaptive progressive thresholding: a hardware perspective

Hardware realization of a novel technique based on adaptive progressive thresholding (APT) for the real-time segmentation of endoscopic images is presented. The APT algorithm is mapped onto a linear array of processing elements with each element of a particular segment communicating with its nearest neighbours. The efficiency and hardware portability of this technique justifies its use in applications that require high performance in real-time.

Automatic segmentation algorithm for the extraction of lumen region and boundary from endoscopic images.

A new segmentation algorithm for lumen region detection and boundary extraction from gastro-intestinal (GI) images is presented. The proposed algorithm consists of two steps. First, a preliminary region of interest (ROI) representing the GI lumen is segmented by an adaptive progressive thresholding (APT) technique. Then, an adaptive filter, the Iris filter, is applied to the ROI to determine the actual region. It has been observed that the combined APT-Iris filter technique can enhance and detect the unclear boundaries in the lumen region of GI images and thus produces a more accurate lumen region, compared with the existing techniques. Experiments are carried out to determine the maximum error on the extracted boundary with respect to an expert-annotated boundary technique. Investigations show that, based on the experimental results obtained from 50 endoscopic images, the maximum error is reduced by up to 72 pixels for a 256 x 256 image representation compared with other existing techniques. In addition, a new boundary extraction algorithm, based on a heuristic search on the neighbourhood pixels, is employed to obtain a connected single pixel width outer boundary using two preferential sequence windows. Experimental results are also presented to justify the effectiveness of the proposed algorithm.

A fast and accurate segmentation technique for the extraction of gastrointestinal lumen from endoscopic images

A novel region-growing algorithm for the segmentation of endoscopic images is proposed in this paper. The objective of the research work is fast and accurate segmentation of gastrointestinal lumen from the endoscopic images for real-time applications. The proposed technique consists of a dual-step methodology in which a quasi Region of Interest (qROI) is segmented first using a global thresholding technique and then the actual lumen is extracted using differential region growing. An adaptive progressive thresholding technique is used to obtain qROI for a given endoscopic image. The centre of mass of qROI acts as the seed for the region growing in the next step. A differential region growing technique is suggested which grows the region on the basis of a similarity criterion. A dynamic hill-clustering method is utilised to ensure the effectiveness of the terminating condition during the growth process. The proposed scheme is faster than the conventional gradient based region-growing technique. The accuracy and high speed response of the proposed technique is validated with several endoscopic images and the results are presented.

A pipelined architecture for image segmentation by adaptive progressive thresholding

A special purpose VLSI architecture for the real-time segmentation of endoscopic images is proposed in this paper. The architecture is based on pipelined implementation of a new algorithm named adaptive progressive thresholding (APT) that segments the darkest region of an endoscopic image representing the gastrointestinal lumen. This segmentation process is an extension of a comprehensive statistical technique based on linear discriminant analysis for partitioning the image. The APT algorithm is mapped onto a linear pipelined array of simple processing elements with each element of a particular segment communicating with its neighbours. The APT architecture is partitioned based on various sequential functions involved in the segmentation process and these functional modules are organised in a pipelined fashion according to their hardware feasibility. Currently, a prototype of the VLSI architecture for an image size of 256×256 is designed and built. The functional simulation results obtained in the APT architecture are encouraging.