Importance of Machine Vision Framework with Nondestructive Approach for Fruit Classification and Grading: A Review

Machine vision technology has gained significant importance in the agricultural industry, particularly in the non-destructive classification and grading of fruits. This paper presents a comprehensive review of the existing literature, highlighting the crucial role of machine vision in automating the fruit quality assessment process. The study encompasses various aspects, including image acquisition techniques, feature extraction methods, and classification algorithms. The analysis reveals the substantial progress made in the field, such as developing sophisticated hardware and software solutions, which have improved accuracy and efficiency in fruit grading. Furthermore, it discusses the challenges and limitations, such as dealing with variability in fruit appearance, handling different fruit types, and real-time processing. The identification of future research needs emphasizes the potential for enhancing machine vision frameworks through the integration of advanced technologies like deep learning and artificial intelligence.Additionally, it underscores the importance of addressing the specific needs of different fruit varieties and exploring the applicability of machine vision in real-world scenarios, such as fruit packaging and logistics. This review underscores the critical role of machine vision in non-destructive fruit classification and grading, with numerous opportunities for further research and innovation. As the agricultural industry continues to evolve, integrating machine vision technologies will be instrumental in improving fruit quality assessment, reducing food waste, and enhancing the overall efficiency of fruit processing and distribution

Active Contours and Image Segmentation: The Current State Of the Art

Image segmentation is a fundamental task in image analysis responsible for partitioning an image into multiple sub-regions based on a desired feature. Active contours have been widely used as attractive image segmentation methods because they always produce sub-regions with continuous boundaries, while the kernel-based edge detection methods, e.g. Sobel edge detectors, often produce discontinuous boundaries. The use of level set theory has provided more flexibility and convenience in the implementation of active contours. However, traditional edge-based active contour models have been applicable to only relatively simple images whose sub-regions are uniform without internal edges. Here in this paper we attempt to brief the taxonomy and current state of the art in Image segmentation and usage of Active Contours

Projecting Active Contours with Diminutive Sequence Optimality

Active contours are widely used in image segmentation. To cope with missing or misleading features in image frames taken in contexts such as spatial and surveillance, researchers have commence various ways to model the preceding of shapes and use the prior to constrict active contours. However, the shape prior is frequently learnt from a large set of annotated data, which is not constantly accessible in practice. In addition, it is often doubted that the existing shapes in the training set will be sufficient to model the new instance in the testing image. In this paper we propose to use the diminutive sequence of image frames to learn the missing contour of the input images. The central median minimization is a simple and effective way to impose the proposed constraint on existing active contour models. Moreover, we extend a fast algorithm to solve the projected model by using the hastened proximal method. The Experiments done using image frames acquired from surveillance, which demonstrated that the proposed method can consistently improve the performance of active contour models and increase the robustness against image defects such as missing boundaries

EASND: Energy Adaptive Secure Neighbour Discovery Scheme for Wireless Sensor Networks

Wireless Sensor Network (WSN) is defined as a distributed system of networking, which is enabled with set of resource constrained sensors, thus attempt to providing a large set of capabilities and connectivity interferences. After deployment nodes in the network must automatically affected heterogeneity of framework and design framework steps, including obtaining knowledge of neighbor nodes for relaying information. The primary goal of the neighbor discovery process is reducing power consumption and enhancing the lifespan of sensor devices. The sensor devices incorporate with advanced multi-purpose protocols, and specifically communication models with the pre-eminent objective of WSN applications. This paper introduces the power and security aware neighbor discovery for WSNs in symmetric and asymmetric scenarios. We have used different of neighbor discovery protocols and security models to make the network as a realistic application dependent model. Finally, we conduct simulation to analyze the performance of the proposed EASND in terms of energy efficiency, collisions, and security. The node channel utilization is exceptionally elevated, and the energy consumption to the discovery of neighbor nodes will also be significantly minimized. Experimental results show that the proposed model has valid accomplishment

Mood & Emotion Detection: Whistling Ball Movement Game

In this investigation, a modified and improved version of the ball-throwing game is presented. The goal is to boost mood and promote mental health utilizing the game system Ball movement and music therapy. The user's whistle pitch determines how the ball moves in this paper. Whistling can serve as a stimulant for the medical reduction of stress, which makes it useful for rehabilitation. Our algorithm adjusts the ball movement performance dependent on the player's whistling score after analyzing it. This article focuses on the system's development and operations

Approaches for User Image Search Goals Using Grouping Similarity

Abstract -Image retrieval and re-ranking as per user image query has become the popular and effective of image retrieval techniques. Similar user query and click through log is important for the success of an image search engine. User search goal analysis will also enhance user experience of a search engine. Using this as a base and leveraging click logs we propose a new design in this paper. In this paper, we focus on designing a new machine learning approach for auto classification and grouping similar user queries for image search system to address a specific kind of image search. Our approach finds most relevant images for a user based on a given user query. Here, our focus is to evaluate the effective association between User Queries and Click through data and customizes search results according to each individual preferences/interests. We also present a ranking procedure to score the images that are retrieved using the proposed approach. I. INTRODUCTION The image retrieval is the process of retrieving images with respect to user intention from the large amount of databases. The user first enters query, based on the keywords in the query the search is performed and from the pool of images resulting images are displayed to the user. Initial image search is performed using surrounding text information of the image. The surrounding text information includes filename, caption or description of the image. e.g. If user wants to search an image of animal tiger and enters query as "tiger" then images containing text "tiger" into their surrounding information are displayed. If any image contains text "tiger" into their surrounding text but it is irrelevant to user intention then this image is also displayed. e.g. image of a person having caption "tiger" is also displayed. The performance of the search decreases because of ambiguous surrounding informatio