K. Vijayan Asari

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 × 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.

Design of an efficient VLSI architecture for non-linear spatial warping of wide-angle camera images

Endoscopic images are subjected to spatial distortion due to the wide-angle configuration of the camera lenses. This barrel type of non-linear distortion should be corrected before these images are subjected to further analysis for diagnostic purposes. An efficient digital architecture suitable for an embedded system which can correct the barrel distortion in real-time is presented in this paper. The theoretical approach of this spatial warping technique is based on least-squares estimation. The images in the distorted image space are mapped onto the corrected image space by using a polynomial mapping model. The polynomial parameters include the expansion coefficients, back-mapping coefficients, distortion centre and corrected centre. Several experiments were conducted by applying the spatial warping algorithm on many endoscopic images. A digital architecture suitable for hardware implementation of the distortion correction technique is developed by mapping the algorithmic steps onto a linear array of processing modules. Each module of a particular unit communicates with its nearest neighbours. The spatial warping architecture implemented and simulated with Alteras Quartus II software shows an overall computation time of 1.8 ms with 50 MHz clock for an image of size 256×192 pixels, which confirms that the spatial warping module could be mounted as a dedicated unit in an endoscopy system for real-time applications.

An Adaptive and Non Linear Technique for Enhancement of Extremely High Contrast Images

In night time surveillance, there is a possibility of having extremely bright and dark regions in some image frames of a video sequence. A novel non linear image enhancement algorithm for digital images captured under such extremely non-uniform lighting conditions is proposed in this paper. The new technique constitutes three processes viz. adaptive intensity enhancement, contrast enhancement and color restoration. Adaptive intensity enhancement uses a specifically designed nonlinear transfer function which is capable of reducing the intensity of bright regions and at the same time enhancing the intensity of dark regions. Contrast enhancement tunes the intensity of each pixels magnitude based on its surrounding pixels. Finally, a linear color restoration process based on the chromatic information of the input image frame is applied to convert the enhanced intensity image back to a color image.

Optical pattern recognition using multiple phase- shifted-reference fringe-adjusted joint transform correlation

We propose a novel optical pattern recognition system using multiple phase-shifted-reference fringe-adjusted joint transform correlation (MRFJTC) techniques. The MRFJTC algorithm can efficiently detect an object of interest in the input scene by producing a highly distinctive correlation peak while rejecting any and all nontarget objects in a complex background. The simple architecture of the proposed system can simultaneously recognize multiple targets of the reference class when present. The recognition performance is fast, automatic, and invariant to noise and distortions.

Optical cryptographic system employing multiple reference-based joint transform correlation technique

An optical joint transform correlation-based cryptographic system is a used to feed multiple phase-shifted encryption keys into four parallel channels along with a to-be-encrypted signal in the form of an image. The resulting joint power spectra (JPS) signals are phase-shifted and then combined to yield a modified JPS signal. Inverse Fourier transformation of the modified JPS signal yields the secured encrypted image. For decryption purpose, the received encrypted signal is first Fourier transformed and multiplied by the encryption key used in encryption. The derived signal is then inverse Fourier transformed to generate the output signal. The proposed system offers a nonlinear encryption without the involvement of any complex mathematical operation on the encryption key otherwise required in similar encryption techniques and is invariant to noise. Computer simulation results are presented to show the effectiveness of the proposed scheme with binary, as well as gray images in both noise-free and noisy environment.

Hull convexity defects features for human activity recognition

Activity recognition has been applied to many varied applications ranging from surveillance to medical analysis. Interpreting human actions is often a complex problem for computer vision. Actions can be classified through shape, motion or region based algorithms. While all have their distinct advantages, we consider a feature extraction approach using convexity defects. This algorithmic approach offers a unique method for identifying actions by extracting features from hull convexity defects. Specifically, we create a hull around the segmented silhouette of interest in which the regions that exist in the hull are recognized. A feature database is created through a dataset of features for multiple individuals. These feature points are registered between progressive frames and then normalized for analysis. Using Principal Component Analysis (PCA), the feature points are classified to different poses. From there testing and training is performed to observe the classification into major human activities. This approach offers a robust and accurate method to identify actions and is invariant to size and human shape.

Anomaly based vessel detection in visible and infrared images

Detection of small vessels is a challenging task for navy, coast guard and port authority for security purposes. Vessel identification is more complex as compared to other object detection because of its variability in shapes, features and orientations. Current methods for vessel detection are primarily based on segmentation techniques which are not as efficient and also require different algorithms for visible and infrared images. In this paper, a new vessel detection technique is proposed employing anomaly detection. The input intensity image is first converted to feature space using difference of Gaussian filters. Then a detector filter in the form of Mahalanobis distance is applied to the feature points to detect anomalies whose characteristics are different from their surroundings. Anomalies are detected as bright spots in both visible and infrared image. The larger the gray value of the pixels the more anomalous they are to be. The detector output is then post-processed and a binary image is constructed where the boat edges with strong variance relative to the background are identified along with few outliers from the background. The resultant image is then clustered to identify the location of the vessel. The main contribution in this paper is developing an algorithm which can reliably detect small vessels in visible and infrared images. The proposed method is investigated using real-life vessel images and found to perform excellent in both visible and infrared images with the same system parameters.

Pattern recognition using Gaussian-filtered, shifted phase-encoded fringe-adjusted joint transform correlation

Pattern recognition for real-time applications requires the detection scheme be a simple architecture, fast in operation, able to detect all the potential targets without generating any false alarms, and invariant to noise and distortion. Though several target detection algorithms have been proposed in the literature over the years, but most of them are found to be not as efficient in meeting all the above-mentioned objective requirements. A new Gaussian-filtered, shifted phase-encoded fringe-adjusted joint transform correlation technique has been developed in this paper for an optical pattern recognition system. The input noisy image is first filtered by using a Gaussian filter, which helps in overcoming the effect of background noise and distortions. Then the filtered image is correlated with the reference image using the proposed joint transform correlator, which eliminates the problems of duplicate correlation heights, false alarms and low discrimination ratio. The architecture involves optical devices including lenses and spatial light modulators, which guarantees the very fast operation required for real-time applications. Computer simulation results show that the algorithm can successfully discriminate between targets and non-targets contained in the input scene even in the presence of noise and can also make the best utilization of the correlation space.

Distortion-invariant pattern recognition using synthetic discriminant function-based shifted phase-encoded joint transform correlator

A novel pattern recognition technique is proposed that employs shifted phase-encoded fringe-adjusted joint transform correlation (SPFJTC) for efficient real-time application and synthetic discriminant function for invariance to distortions. The proposed technique produces a single delta-like correlation for a potential target in the input scene, and performs successfully even in a noisy environment. The simple architecture of the proposed technique can be implemented easily on optoelectronics for very high-speed operation. Computer simulation results verify the efficient performance of the technique under different variations of the input scene and the environment.

Distortion-invariant face recognition using multiple phase-shifted reference-based joint transform correlation technique

We have developed a novel face recognition technique utilizing optical joint transform correlation (JTC) technique which provides with a number of salient features as compared to similar other digital techniques, including fast operation, simple architecture and capability of updating the reference image in real time. The proposed technique incorporates a synthetic discriminant function (SDF) of the target face estimated from a set of different training faces to make the face recognition performance invariant to noise and distortion. The technique then involves four different phase-shifted versions of the same SDF reference face, which are individually joint transform correlated with the given input scene with unknown faces and other objects. Appropriate combination of correlation signals yields a single cross-correlation peak corresponding to each potential face image. The technique also involves a fringe-adjusted filter to generate a delta-like correlation peak with high discrimination between the target face and the non-target face and background objects. Performance of the proposed face recognition technique is investigated through computer simulation where it is observed to be efficient and successful in different complex environments.