Vishal R. Satpute

Bio: Vishal R. Satpute is an academic researcher from Visvesvaraya National Institute of Technology. The author has contributed to research in topics: Discrete wavelet transform & Video tracking. The author has an hindex of 9, co-authored 56 publications receiving 277 citations.

A novel bit permutation-based image encryption algorithm

Abstract: In recent years, substantial work has been done for developing image encryption algorithms. Image encryption requires handling of large data, which needs computationally efficient algorithm. Chaos-based image encryption has been proposed against conventional encryption techniques. Cryptosystem using chaotic systems for image encryption has proven to be computationally effective. In this paper, a new approach to bit permutation in image encryption has been proposed using a three-dimensional puzzle along with chaos for further diffusion and confusion. The proposed encryption algorithm is tested for security and validity using various analyses. The result for tests proves that the proposed algorithm provides security against statistical and differential attacks.

Global-patch-hybrid template-based arbitrary object tracking with integral channel features

Abstract: Arbitrary object tracking is a challenging task in computer vision, as many factors affecting the target representation must be considered. A target template based on only the global appearance or on only the local appearance is unable to capture the discriminating information required for the robust performance of a tracker. In this paper, the target appearance is represented using a hybrid of global and local appearances along with a framework to exploit the Integral Channel Features (ICF). The proposed hybrid approach achieves fusion of the conventional global and patch-based approaches for target representation to synergize the advantages of both approaches. The ICF approach under the hybrid approach integrates heterogeneous sources of information of the target and provides feature strength to the hybrid template. The use of ICF also expedites the extraction of the structural and color features from video frames as the features are collected over multiple channels. The target appearance representation is updated based on only samples with appearances similar to the target appearance using clustering and vector quantization. These factors offer the proposed algorithm robustness to occlusion, illumination changes, and in-plane rotation. Further experimentation analyzes the effects of a change in the scale of the bounding box on the tracking performance of the proposed algorithm. The proposed approach outperforms all the state-of-the-art algorithms in all considered scenarios.

Noise-resilient compressed domain video watermarking system for in-car camera security

Abstract: In-car camera system emerges as a very useful technology for automobile drivers, as it can provide proper evidence to insurance companies and to police investigators in case of car accidents. Dashcam, being one of the car video storage devices, stores the data in the SD card which is overwritten and can be tampered very easily. Thus, it is important to develop an in-car security system, where data can be stored in the server and can provide privacy to the user. In this paper, we have proposed a noise-resilient compressed domain video watermarking system for in-car security to document the vehicle history which can be a proof for any mischief. We have used Independent Pass Coding (INPAC) compression technique for designing the system. Here, data generated by the system is stored in the server database which is accessible to the authentic registered user and administrator in case of any claim. This system ensures copyright, proprietorship, authentication and security against third party intrusion. The proposed system is implemented in off-line mode to evaluate the robustness and efficiency of the algorithm, and in online mode to verify the system. Graphical user interface is designed for the end user access, to make it user friendly.

A novel approach based on variance for local feature analysis of facial images

Abstract: A low dimensional representation of sensory signals is a key for solving many of the computational problems encountered in high level vision. In this paper, a comparison of face recognition techniques using principal component analysis (PCA) is done with local feature analysis (LFA) and an alternate method based on variance for quickly finding the local feature points on face images is also proposed. The LFA method is an extension of the eigenfaces method and gives a low-dimensional output for face representation. Principal component analysis (PCA) that is used for dimensionality reduction in the eigenfaces technique leads to global outputs, which are non-topographic and are not biologically plausible. On the other hand, the local feature analysis (LFA) technique yields local, topographic outputs which are sparsely distributed. They are effectively low dimensional but retain all the characteristics of the global modes. Local representations are desirable since they offer robustness against variability due to changes in the localised regions of the objects. A strategy for recognising faces using LFA is also proposed and several results on reconstruction and recognition are given to compare the performance of the variance method with that of LFA and PCA.

[서평]「Applied Cryptography」

Abstract: The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

IEEE transactions on pattern analysis and machine intelligence

Abstract: In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to: • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information. Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

A Survey on the Role of IoT in Agriculture for the Implementation of Smart Farming

Abstract: Internet of things (IoT) is a promising technology which provides efficient and reliable solutions towards the modernization of several domains. IoT based solutions are being developed to automatically maintain and monitor agricultural farms with minimal human involvement. The article presents many aspects of technologies involved in the domain of IoT in agriculture. It explains the major components of IoT based smart farming. A rigorous discussion on network technologies used in IoT based agriculture has been presented, that involves network architecture and layers, network topologies used, and protocols. Furthermore, the connection of IoT based agriculture systems with relevant technologies including cloud computing, big data storage and analytics has also been presented. In addition, security issues in IoT agriculture have been highlighted. A list of smart phone based and sensor based applications developed for different aspects of farm management has also been presented. Lastly, the regulations and policies made by several countries to standardize IoT based agriculture have been presented along with few available success stories. In the end, some open research issues and challenges in IoT agriculture field have been presented.

Precision Agriculture Techniques and Practices: From Considerations to Applications.

Abstract: Internet of Things (IoT)-based automation of agricultural events can change the agriculture sector from being static and manual to dynamic and smart, leading to enhanced production with reduced human efforts. Precision Agriculture (PA) along with Wireless Sensor Network (WSN) are the main drivers of automation in the agriculture domain. PA uses specific sensors and software to ensure that the crops receive exactly what they need to optimize productivity and sustainability. PA includes retrieving real data about the conditions of soil, crops and weather from the sensors deployed in the fields. High-resolution images of crops are obtained from satellite or air-borne platforms (manned or unmanned), which are further processed to extract information used to provide future decisions. In this paper, a review of near and remote sensor networks in the agriculture domain is presented along with several considerations and challenges. This survey includes wireless communication technologies, sensors, and wireless nodes used to assess the environmental behaviour, the platforms used to obtain spectral images of crops, the common vegetation indices used to analyse spectral images and applications of WSN in agriculture. As a proof of concept, we present a case study showing how WSN-based PA system can be implemented. We propose an IoT-based smart solution for crop health monitoring, which is comprised of two modules. The first module is a wireless sensor network-based system to monitor real-time crop health status. The second module uses a low altitude remote sensing platform to obtain multi-spectral imagery, which is further processed to classify healthy and unhealthy crops. We also highlight the results obtained using a case study and list the challenges and future directions based on our work.

IoT-Based Smart Irrigation Systems: An Overview on the Recent Trends on Sensors and IoT Systems for Irrigation in Precision Agriculture

Abstract: Water management is paramount in countries with water scarcity. This also affects agriculture, as a large amount of water is dedicated to that use. The possible consequences of global warming lead to the consideration of creating water adaptation measures to ensure the availability of water for food production and consumption. Thus, studies aimed at saving water usage in the irrigation process have increased over the years. Typical commercial sensors for agriculture irrigation systems are very expensive, making it impossible for smaller farmers to implement this type of system. However, manufacturers are currently offering low-cost sensors that can be connected to nodes to implement affordable systems for irrigation management and agriculture monitoring. Due to the recent advances in IoT and WSN technologies that can be applied in the development of these systems, we present a survey aimed at summarizing the current state of the art regarding smart irrigation systems. We determine the parameters that are monitored in irrigation systems regarding water quantity and quality, soil characteristics and weather conditions. We provide an overview of the most utilized nodes and wireless technologies. Lastly, we will discuss the challenges and the best practices for the implementation of sensor-based irrigation systems.