Sapan Naik

Bio: Sapan Naik is an academic researcher from Uka Tarsadia University. The author has contributed to research in topics: Wavelet & Image (mathematics). The author has an hindex of 11, co-authored 21 publications receiving 292 citations.

Machine Vision based Fruit Classification and Grading - A Review

Abstract: One of the important quality features of fruits is its appearance. Appearance not only influences their market value, the preferences and the choice of the consumer, but also their internal quality to a certain extent. Color, texture, size, shape, as well the visual flaws are generally examined to assess the outside quality of food. Manually controlling external quality control of fruit is time consuming and laborintensive. Thus for automatic external quality control of food and agricultural products, computer vision systems have been widely used in the food industry and have proved to be a scientific and powerful tool for by intensive work over decades. The use of machine and computer vision technology in the field of external quality inspection of fruit has been published based on studies carried on spatial image and / or spectral image processing and analysis. A detailed overview of the process of fruit classification and grading has been presented in this paper. Detail examination of each step is done. Some extraction methods like Speeded Up Robust Features (SURF), Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) are discussed with the common features of fruits like color, size, shape and texture. Machine learning algorithms like K-nearest neighbor (KNN), Support Vector Machine (SVM), Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) are also discussed. Process, advantages, disadvantages, challenges occurring in food-classification and grading is discussed in this paper, which can give direction to researchers. General Terms Machine Vision, Fruit Classification, Grading.

Image Processing and Machine Learning for Automated Fruit Grading System: A Technical Review

Abstract: In India, demand for various fruits and vegetables are increasing as population grows.Automation in agriculture plays a vital role in increasing the productivity and economical growth of the Country, therefore there is a need for automated system for accurate, fast and quality fruits determination. Researchers have developed numerous algorithms for quality grading and sorting of fruit. Color is most striking feature for identifying disease and maturity of the fruit. In this paper; efficient algorithms for color feature extraction are reviewed. Then after, various classification techniques are compared based on their merits and demerits. The objective of the paper is to provide introduction to machine learning and color based grading algorithms, its components and current work reported on an automatic fruit grading system.

Evaluation of Different Image Interpolation Algorithms

Abstract: Interpolation is the process of transferring image from one resolution to another without losing image quality. In Image processing field, image interpolation is very important function for doing zooming, enhancement of image, resizing any many more. Here in this paper, we have reviewed different interpolation algorithms. We have implemented all reviewed interpolation algorithms and done comparison of all.

Thermal imaging with fuzzy classifier for maturity and size based non-destructive mango (Mangifera Indica L.) grading

Abstract: This is the era of ICT technologies. As it's an important task to reach consumer's demand for good quality mango, automation in grading of mango (Mangifera Indica L.) is required. This paper is addressing the grading issue of agricultural produce based on its maturity and size. The Fuzzy inference system is used for decision-making process. Prediction of mango's maturity is done through its skin's color but for some exceptional tribe of mango like “Langdo”, skin color will remain the same for its lifetime. In such cases, normal imaging (reflective imaging) will not work for predicting its maturity. One can use infrared, x-ray or thermal imaging for maturity prediction. Here mango grading is performed based on maturity and size feature. For that, with mean intensity algorithm in L∗a∗b∗ color space and FLIR ONE thermal camera is used to predict the maturity of mango. Size of mangois predicted by three parameters namely weight, eccentricity and area. Fuzzy classifier is used for predicting size feature. To grade mango decision making theory is used and mango is graded in two different classes. Time needed to grade a mango is 2.3 seconds and accuracy received is 89%.

Single image super resolution in spatial and wavelet domain

Abstract: Recently single image super resolution is very important research area to generate high resolution image from given low resolution image. Algorithms of single image resolution are mainly based on wavelet domain and spatial domain. Filters support to model the regularity of natural images is exploited in wavelet domain while edges of images get sharp during up sampling in spatial domain. Here single image super resolution algorithm is presented which based on both spatial and wavelet domain and take the advantage of both. Algorithm is iterative and use back projection to minimize reconstruction error. Wavelet based denoising method is also introduced to remove noise.

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.

Wavelet-SRNet: A Wavelet-Based CNN for Multi-scale Face Super Resolution

Abstract: Most modern face super-resolution methods resort to convolutional neural networks (CNN) to infer highresolution (HR) face images. When dealing with very low resolution (LR) images, the performance of these CNN based methods greatly degrades. Meanwhile, these methods tend to produce over-smoothed outputs and miss some textural details. To address these challenges, this paper presents a wavelet-based CNN approach that can ultra-resolve a very low resolution face image of 16 × 16 or smaller pixelsize to its larger version of multiple scaling factors (2×, 4×, 8× and even 16×) in a unified framework. Different from conventional CNN methods directly inferring HR images, our approach firstly learns to predict the LR’s corresponding series of HR’s wavelet coefficients before reconstructing HR images from them. To capture both global topology information and local texture details of human faces, we present a flexible and extensible convolutional neural network with three types of loss: wavelet prediction loss, texture loss and full-image loss. Extensive experiments demonstrate that the proposed approach achieves more appealing results both quantitatively and qualitatively than state-ofthe- art super-resolution methods.