In this paper, an effort is made to enlarge a low resolution image. This paper presents an effective novel single image super resolution approach to recover a high resolution image from a single low resolution input image. The approach is based on an Iterative back projection (IBP) method combined with the Canny Edge Detection and error difference image to recover high frequency information. This method is applied on different natural gray images and compared with different existing image super resolution approaches. Simulation results show that the proposed algorithm can more accurately enlarge the low resolution image than previous approaches. Proposed algorithm increases the PSNR and decreases MSE and MAE compared to other existing algorithms and also improves visual quality of an image considerably.

Dental X-ray has played an important role in identification of missing or unidentified persons. Particularly in cases where other identification clues like fingerprint, iris, etc. are not available and, moreover, dental features remain more or less invariant over time. The purpose of dental image processing is to match the Post-mortem (PM) radiograph with the Antemortem (AM) radiograph based on some unique feature of the radiograph. The first step is to enhance the quality of image and region of interest can be separated. Unique features of a tooth are extracted and identification is performed based on matching of these feature vectors of PM images with those of AM images available in the database. A new feature based on triangular geometry of the tooth has been proposed and thereafter matching of query and database images is performed for identification of the subject. This feature is called the tooth taper parameter.

The Agricultural industry on the whole is ancient so far. Quality assessment of grains is a very big challenge since time immemorial. The paper presents a solution for quality evaluation and grading of Rice industry using computer vision and image processing. In this paper basic problem of rice industry for quality assessment is defined which is traditionally done manually by human inspector. Machine vision provides one alternative for an automated, non-destructive and cost-effective technique. With the help of proposed method for solution of quality assessment via computer vision, image analysis and processing there is a high degree of quality achieved as compared to human vision inspection. This paper proposes a new method for counting the number of Oryza sativa L (rice seeds) with long seeds as well as small seeds using image processing with a high degree of quality and then quantify the same for the rice seeds based on combined measurements.

A number of techniques have been proposed in the past for automatic quality evaluations of pre-processed tobacco using image processing. Although some studies have aimed to evaluate the quality of processed tobacco. There is no automatic system that is capable of evaluating the processed tobacco. This paper proposes a new method for counting the number of normal Chewing tobacco (Nicotiana tabacum) as well as foreign elements using machine vision. By proposed method a quality evaluation of processed chewing tobacco can be done which would be very beneficial for the purpose of its quality which is ready to be eat product.

The Agricultural industry on the whole is ancient so far. Quality assessment of grains is a very big challenge since time immemorial. The paper presents a solution for quality evaluation and grading of Rice industry using computer vision and image processing. In this paper basic problem of rice industry for quality assessment is defined which is traditionally done manually by human inspector. Machine vision provides one alternative for an automated, non-destructive and cost-effective technique. With the help of proposed method for solution of quality assessment via computer vision, image analysis and processing there is a high degree of quality achieved as compared to human vision inspection. This paper proposes a new method for counting the number of Oryza sativa L (rice seeds) with long seeds as well as small seeds using image processing with a high degree of quality and then quantify the same for the rice seeds based on combined measurements.

A number of techniques have been proposed in the past for automatic quality evaluations of pre–processed tobacco using image processing. Although some studies have aimed to evaluate the quality of processed tobacco. There is no automatic system that is capable of evaluating the processed tobacco. This paper proposes a new method for counting the number of normal FCV tobacco (Nicotiana tabacum) as well as foreign elements using machine vision. By proposed method a quality evaluation of processed FCV tobacco can be done which would be very beneficial for the purpose of its quality which is ready to be eat product.

Dental image processing is most immerging field for human identification. Dental features remain more or less invariant over time compared to other identification clues like fingerprint, iris, etc. which are not available in some case of major accidents. The purpose of dental image processing is to match the post-mortem (PM) radiograph with the ante mortem (AM) radiograph based on some characteristic or feature of the radiograph for human identification. Image enhancement is necessary because of poor quality and low contrast of dental image at primary stage. Thereafter segmentation algorithms are applied to the enhanced dental x-ray image which helps to find two major regions namely gap valley and tooth isolation. The main crucial part is tooth and feature extraction of dental image. In this paper, we propose a simple and novel CBIR technique to extract individual tooth and thereafter we extract geometrical features of dental x-ray radiographs for human identification purpose. We compared feature vectors of database with query image and calculated the distance vector for matching purpose.

Compressive sensing (CS) technique addresses the issue of compressing the sparse signal with a rate below Nyquist rate of sampling. For medical images there are always issues of acquisition time and compression, the compressive sensing is found to be a better technique that works in a manner that it first acquires samples less than signal dimensionality and reconstructs the same signal. In this paper Wavelet transform is applied along with compressive sensing on CT images. Three various measurements (for three compression ratio values) have been taken and calculated PSNR, CoC, and RMSE. As measurements are increased PSNR, CoC and visual quality increases and RMSE decreases. The main observation is that only 60% measurements can reproduce image with PSNR of more than 25 dB and with CoC more than 0.99.

The use of digital medical images is increasing very fast. Medical images, like CT scan, Ultrasound, Dental X-ray etc, require large amounts of memory storage. Even to transmit an image over a wireless or LAN network could take more time. Due to this reason medical image compression is important. Related to medical images many compression methods are available. However, the lossless (for diagnostic and legal reasons) techniques, which allow for perfect reconstruction of the original images, yield compression rates of at most 2 only, while the techniques that yield higher compression rates are lossy. To meet this challenge, we have developed a hybrid compression schemes which is diagnostically lossless with good compression ratio. Due to its simplicity the hardware realisation is also easy and cost effective compare to JPEG method.

Parameter estimation is crucial for model identification in control engineering and in other disciplines. In present work a new method based on interval constraint satisfaction technique is proposed for obtaining parameter estimation of a system using linear orthogonal regression. In orthogonal regression, errors in both the measured variable and response variable are taken into account. The power of interval analysis based method is explored to develop proposed method. Two well known examples of datasets are used to validate the proposed method.

Compressed Sensing (CS) is a novel approach of reconstructing a sparse signal much below the significant Nyquist rate of sampling. Due to the fact that ECG signals can be well approximated by the few linear combinations of wavelet basis, this work introduces a comparison of the reconstructed ECG signal based on different wavelet families, by evaluating the performance measures as MSE (Mean Square Error), PSNR (Peak Signal To Noise Ratio), PRD (Percentage Root Mean Square Difference) and CoC (Correlation Coefficient). Reconstruction of the ECG signal is a linear optimization process which consider the sparsity in the wavelet domain, perceived by the fact that higher the sparsity, more better the recovery. L1 minimization is used as the recovery algorithm. The reconstruction results are comprehensively analyzed for five compression ratios, i.e. 2:1, 4:1, 6:1, 8:1 and 10:1. The results indicate that reverse biorthogonal wavelet family can give better results for all (Compression Ratio's)CRs compared to other families.

In any image processing system denoising of images is an important step. The images can be corrupted by different noises with different levels. There are three types of noises available: impulse, Gaussian and Speckle noises with mixture of them. Many algorithms are proposed to remove salt & pepper (impulse) noise as well as Gaussian noise. The Robust statistics based filter is also proposed to remove either impulse or Gaussian noise using Lorentian rho function based robust M estimator. However, there is still a need to find a most efficient filter for image denoising, which can be effective for salt & pepper noise with different noise levels. In this paper we evaluate the performance of MM-estimator and M-estimator based image denoising filters for salt & pepper noise only. The results show very good impulse noise removal by MM estimator compared to M-estimator.

Compressed Sensing (CS) is a novel approach of reconstructing a sparse signal much below the significant Nyquist rate of sampling. Due to the fact that ECG signals can be well approximated by the few linear combinations of wavelet basis, this work introduces a comparison of the reconstructed 10 ECG signals based on different wavelet families, by evaluating the performance measures as MSE (Mean Square Error), PSNR (Peak Signal To Noise Ratio), PRD (Percentage Root Mean Square Difference) and CoC (Correlation Coefficient). Reconstruction of the ECG signal is a linear optimization process which considers the sparsity in the wavelet domain. L1 minimization is used as the recovery algorithm. The reconstruction results are comprehensively analyzed for three compression ratios, i.e. 2:1, 4:1, and 6:1. The results indicate that reverse biorthogonal wavelet family can give better results for all CRs compared to other families.

Identification of human based on iris has gained increased attention in recent years. The paper focuses on novel and efficient approach of partial iris based recognition of human using pupil circle region growing and binary integrated edge intensity curve which defeats the difficulties of eyelids occlusions. The experimental results are obtained on CASIA database version-1 and show good performance with EER of 5.14%. The advantage of the proposed approach is its computational simplicity and good recognition accuracy as it avoids the eyelids portion from the iris region for further processing.

In this paper location M estimator is used to inspect the over and under fill liquid level of bottle in machine vision system. Different optimal edge detection algorithms such as MarrHilderth algorithm LoG, Canny algorithm and Shen Castan algorithm are used for liquid level inspection in industry until now. The filling level in a bottle is computed as an average distance from a specific reference line. The average operator is not robust to outliers in the data. In this paper, we propose to use the combination of location M estimator with any type of edge detection technique which will remove the need of a specific optimal edge detection technique and thus can result into easy hardware realization of the liquid level inspection algorithm.

In recent years, the need for personal identification systems has increased in cases of severe accidents and calamities for criminal investigation. Biometric identification systems can be used for gaining access to systems and verification purpose. However, biometrics such as iris, fingerprint, etc. are vulnerable to early decay and decomposition. On the other hand, dental x rays remain invariant over time and resist early decomposition, fire, etc. Hence they can be used for human identification purpose. In this paper, a novel approach to feature extraction of dental x-ray radiograph is proposed based on the shape and texture of extracted tooth from the radiograph and thereafter matching is done by finding mean square error between the query and database images.

This paper addresses the problem of recovering a super-resolved image from a single low resolution input. This is a hybrid approach of single image super resolution. The technique is based on combining an Iterative back projection (IBP) method with the edge preserving Infinite symmetrical exponential filter (ISEF). Though IBP can minimize the reconstruction error significantly in iterative manner and gives good result, it suffers from ringing effect and chessboard effect because error is back-projected without edge guidance. ISEF provides edge-smoothing image by adding high frequency information. Proposed algorithm integrates ISEF with IBP which improves visual quality with very fine edge details. The method is applied on different type of images including face image, natural image and medical image, the performance is compared with a number of other algorithms, bilinear interpolation, nearest neighbor interpolation and Laplacian of Gaussian (LOG). The method proposed in the paper is shown to be marginally superior to the existing method in terms of visual quality and peak signal to noise ratio (PSNR).

The paper presents a unified approach for quality evaluation of food using image processing and machine vision. In this paper basic tool is combination of computer and machine vision for image analysis and processing through which fast and accurate quality is achieved that too with the help of non-destructive method. Machine vision in food has broadened its range of applications from grains, cereals, fruits to vegetables including processed products as well as spices in which there is a high degree of quality achieved as compared to human vision inspection. In this paper we quantify the qualities of various food products and figure out features which are directly or inversely affect the quality of the food product. Based on these features a generalized formula of quality is proposed to be used for quality evaluation of any type of food product.