Deep neural network concepts for background subtraction:A systematic review and comparative evaluation

Many species of insect pests can be detected and monitored automatically. Several systems have been designed in order to improve integrated pest management (IPM) in the context of precision agriculture. Automatic detection traps have been developed for many important pests. These techniques and new technologies are very promising for the early detection and monitoring of aggressive and quarantine pests. The aim of the present paper is to review the techniques and scientific state of the art of the use of sensors for automatic detection and monitoring of insect pests. The paper focuses on the methods for identification of pests based in infrared sensors, audio sensors and image-based classification, presenting the different systems available, examples of applications and recent developments, including machine learning and Internet of Things. Future trends of automatic traps and decision support systems are also discussed.

https://www.mdpi.com/2077-0472/10/5/161/pdf

Automatic Detection and Monitoring of Insect Pests—A Review

Research on insect pest image detection and recognition based on bio-inspired methods

How to build a generic deep one-class (DeepOC) model to solve one-class classification problems for anomaly detection, such as anomalous event detection in complex scenes? The characteristics of existing one-class labels lead to a dilemma: it is hard to directly use a multiple classifier based on deep neural networks to solve one-class classification problems. Therefore, in this article, we propose a novel DeepOC neural network, termed as DeepOC, which can simultaneously learn compact feature representations and train a DeepOC classifier. Only with the given normal samples, we use the stacked convolutional encoder to generate their low-dimensional high-level features and train a one-class classifier to make these features as compact as possible. Meanwhile, for the sake of the correct mapping relation and the feature representations’ diversity, we utilize a decoder in order to reconstruct raw samples from these low-dimensional feature representations. This structure is gradually established using an adversarial mechanism during the training stage. This mechanism is the key to our model. It organically combines two seemingly contradictory components and allows them to take advantage of each other, thus making the model robust and effective. Unlike methods that use handcrafted features or those that are separated into two stages (extracting features and training classifiers), DeepOC is a one-stage model using reliable features that are automatically extracted by neural networks. Experiments on various benchmark data sets show that DeepOC is feasible and achieves the state-of-the-art anomaly detection results compared with a dozen existing methods.

A Deep One-Class Neural Network for Anomalous Event Detection in Complex Scenes

English Character Recognition (CR) has been extensively studied in the last half century and progressed to a level, sufficient to produce technology driven applications. But same is not the case for Indian languages which are complicated in terms of structure and computations. Rapidly growing computational power may enable the implementation of Indic CR methodologies. Digital document processing is gaining popularity for application to office and library automation, bank and postal services, publishing houses and communication technology. Devnagari being the national language of India, spoken by more than 500 million people, should be given special attention so that document retrieval and analysis of rich ancient and modern Indian literature can be effectively done. This article is intended to serve as a guide and update for the readers, working in the Devnagari Optical Character Recognition (DOCR) area. An overview of DOCR systems is presented and the available DOCR techniques are reviewed. The current status of DOCR is discussed and directions for future research are suggested.

A Review of Research on Devnagari Character Recognition

Shape Feature and Fuzzy Logic Based Offline Devnagari Handwritten Optical Character Recognition

The explosive growth in the process control industries has increased the requirement of automatic calibration of devises drastically. In process control industries calibration of different measuring instruments for temperature, voltage, pressure, current, etc. is mostly done manually. Some of the industries use software based approach for calibration. In this paper a generalized module for automatic calibration of any measuring instruments (e.g. Temperature Monitoring System) using optical character recognition approach has been proposed. Module is designed for scanning of seven segment display of measuring instruments through camera. The System will preprocess the seven segment character display images and then extract the features for recognition. This paper mainly suggests algorithms for recognition of seven segment display characters present on digital multi-meters. This solution benefits toward significant reduction in work quantity for calibration as well as from better replication and objectivity of measuring. The accuracy of the system is 95%.

Optical character recognition system for seven segment display images of measuring instruments

In this article, we have tried to explore a new hybrid approach which well integrates the advantages of structural and statistical approaches and avoids their weaknesses. In the proposed approach, the graphic symbols are first segmented into high-level primitive like quadrilaterals. Then, a graph is built by utilizing these quadrilaterals as nodes and their spatial relationships as edges. Additional information like relative length of the quadrilaterals and their relative angles with neighbouring quadrilaterals are associated as attributes to the nodes and edges of the graph respectively. However, the observed graphs are subject to deformations due to noise and/or vectorial distortion (in case of hand-drawn images) hence differs somewhat from their ideal models by either missing or extra nodes and edges appearance. Therefore, we propose a method that computes a measure of similarity between two given graphs instead of looking for exact isomorphism. The approach is based on comparing feature vectors extracted from the graphs. The idea is to use features that can be quickly computed from a graph on the one hand, but are, on the other hand, effective in discriminating between the various graphs in the database. The nearest neighbour rule is used as a classifier due to its simplicity and good behaviour

Combination of Symbolic and Statistical Features for Symbols Recognition

Surface electromyogram (EMG) signals collected from amputee’s residual limb have been utilized to control the prosthetic limb movements for many years. The extensive research has been carried out to classify arm and hand movements by many researchers. However, for control of the more dexterous prosthetic hand, controlling of single and multiple prosthetic fingers needs to be focused. The classification of single and multiple finger movements is challenging as the large number of EMG electrodes/channels are required to classify more number of movement classes. Also the misclassification rate increases significantly with the increased number of finger movements. To enable such control, the most informative and discriminative feature set which can accurately differentiate between different finger movements must be extracted. This work proposes an accurate and novel scheme for feature set extraction and projection based on Sparse Filtering of wavelet packet coefficients. Unlike the existing feature extraction-projection techniques, the proposed method can classify a large number of single and multiple finger movements accurately with reduced hardware complexity. The proposed method is compared to other combinations of feature extraction-reduction methods and validated on EMG dataset collected from eight subjects performing 15 different finger movements. The experimental results show the importance of the proposed scheme in comparison with existing feature extraction-projection schemes with an average accuracy of 99.52% ± 0.6%. The results also indicate that the subset of five EMG channels delivers similar accuracy (>99%) to those obtained from all eight channels. The resultant accuracy values are improved over the existing one reported in the literature, whereas only one-third numbers of channels per identified motions are employed. The experimental results and analysis of variance tests (p < 0.001) prove the feasibility of the proposed work.

Electromyogram (EMG) based fingers movement recognition using sparse filtering of wavelet packet coefficients

The automatic detection and recognition of anomalous events in crowded and complex scenes on video are the research objectives of this paper. The main challenge in this system is to create models for detecting such events due to their changeability and the territory of the context of the scenes. Due to these challenges, this paper proposed a novel HOME FAST (Histogram of Orientation, Magnitude, and Entropy with Fast Accelerated Segment Test) spatiotemporal feature extraction approach based on optical flow information to capture anomalies. This descriptor performs the video analysis within the smart surveillance domain and detects anomalies. In deep learning, the training step learns all the normal patterns from the high-level and low-level information. The events are described in testing and, if they differ from the normal pattern, are considered as anomalous. The overall proposed system robustly identifies both local and global abnormal events from complex scenes and solves the problem of detection under various transformations with respect to the state-of-the-art approaches. The performance assessment of the simulation outcome validated that the projected model could handle different anomalous events in a crowded scene and automatically recognize anomalous events with success.

/pdf/deep-learning-with-a-spatiotemporal-descriptor-of-appearance-89b2y8qb10.pdf

Prachi Mukherji

Papers

Shape Feature and Fuzzy Logic Based Offline Devnagari Handwritten Optical Character Recognition

Optical character recognition system for seven segment display images of measuring instruments

Combination of Symbolic and Statistical Features for Symbols Recognition

Electromyogram (EMG) based fingers movement recognition using sparse filtering of wavelet packet coefficients

Deep Learning with a Spatiotemporal Descriptor of Appearance and Motion Estimation for Video Anomaly Detection