Introduction to Recognition Approach

In this work, a new face recognition approach is proposed using a guided image filter, and a convolutional neural network (CNN).

The proposed method is inspired from action recognition approaches and exploits 3D convolutional neural networks with residual blocks.

A deep convolutional neural network was then trained to convert the LCD images into numerical values using a multi-digit recognition approach.

This paper presents a simple yet effective air-writing recognition approach based on deep convolutional neural networks (CNNs).

The study applies a multilevel pattern recognition approach incorporating time-series assessment, correlation, and complex network analysis based on community detection with the use of the modularity optimization algorithm, on data of overnight-stays recorded for the time-period 1998–2018.

Traditional pattern recognition approaches have gained a lot of popularity.

The experimental findings indicate that the proposed approach outperforms the most current cutting-edge face recognition approaches.

In this paper, we propose a new method to detect and to use the non-occluded parts of face image for modular face recognition approaches.

In this paper, we present a new video-based driver action recognition approach based on the hybrid spatial–temporal deep learning framework.

The proposed method is inspired from action recognition approaches and exploits 3D convolutional neural networks with residual blocks.

Most of the existing human activity recognition approaches based on 3D pose data often enrich the input data using additional handcrafted representations such as velocity, super-normal vectors, pairwise relations, and so on.

The proposed LSTM-based activity recognition approach performed better than conventional approaches, with an accuracy of 99.

So, there is an advance in this field among which Gait recognition approach is in major interest by which person identification has became an easy task.

Most traditional Computer Vision-based gait recognition approaches have been tested to work satisfactorily given similar training and test conditions.

To attain this objective, firstly, a survey on recent vision-based gesture recognition approaches has been carried out.

In this work, we aim to develop an effective surgical gesture recognition approach with an explainable feature extraction process.

The main purpose of this research is to provide an aggregated description of current employment discrimination in Library and Information Science (LIS) job market in Mainland China utilizing entity recognition approach.

Named Entity Recognition approaches in the previous study uses Part-of-Speech (POS) Tagging in the preprocessing phase by using other tools or models.

In this paper, we propose a 3D object recognition approach using a 3D extension of the histogram-of-gradients object descriptor with data captured with a depth camera.

In this paper, an object recognition approach for synthetic aperture radar (SAR) images is addressed, which is based on the enhanced kernel sparse representation of monogenic signal.

This paper presents the state of the art in approaches for immersive experiences in Digital Culture, reviews the current image recognition approaches and their suitability for Byzantine paintings, proposes interaction techniques appropriately designed for observation and interaction of paintings, and discusses the overall framework architecture that is needed to support the described functionality while stressing the challenging issues of the above aspects thus paving the road for future work and providing guidelines for a test-bed implementation.

This paper explains the development of a system to automatically detect the smoking scenes using image recognition approach in video clips and then add the warning message to the viewer.

To address the issue of manual assessment, we propose an instructor's facial expression recognition approach within a classroom using a feedforward learning model.

In this paper, we propose effective facial expression recognition approachfor recognizing six basic facial expressions.

In this study, we develop RLTIR, an interactive identity recognition approach based on reinforcement learning, to adjust the identification model by human guidance.

53% of the existing identity recognition approaches with the same accuracy.

In this study, we present a novel LIDAR sensor-based target recognition approach of the point cloud data, using adaptive rounding algorithm and optimized support vector (SVM).

In this paper, InVision, a robot target recognition approach is proposed using deep federated learning.

A quick description and summary of signal recognition approaches is offered in this study.

In this paper, a brief overview of signal recognition approaches is presented.

Finally, we propose two cognitive state recognition approaches (interest and distraction) in with and without distractor conditions.

To solve this problem, this paper proposes a type process state recognition approach and an adaptive model correction strategy.

Thus, the current research work develops a novel Tamil Handwritten Character Recognition approach by following two major processes, viz.

In this paper, a character recognition approach using convolutional neural networks with specific focus on Tamil palm-leaf characters has been presented.

Combining the advantages from both camera and lidar-based place recognition approaches, our method is truly rotation-invariant, and can tackle reverse revisiting and upside down revisiting.

In this paper, we propose a novel visual place recognition approach with a deep fusion of the salient features and similarity network.

The main aim of this research article is to provide the solution for various handwriting recognition approaches such as touch input from the mobile screen and picture file.

This work presents a novel handwriting recognition approach based on the application of coordinate vectors to find similarities in different kinds of deformations.

In addition, the ontology-based event recognition approach detects traffic events from traffic-related data.

In this paper a novel bottom-up video event recognition approach is proposed, ObjectGraphs, which utilizes a rich frame representation and the relations between objects within each frame.

15% accuracy in the classification of all 26 air-written letters and outperforms the fairly new WiFi-based air-writing recognition approaches.

This paper presents a simple yet effective air-writing recognition approach based on deep convolutional neural networks (CNNs).

We present experiments on accuracy and resource usage and prove that our system realizes a latency-low and power-efficient scene recognition approach by trading off a reasonable performance loss (averaged recall of 92.

The existing RGB-D scene recognition approaches typically employ two separate and modality-specific networks to learn effective RGB and Depth representations respectively.

The Enhanced Cat Swarm Optimization (ECSO) algorithm for feature extraction has been proposed to address these issues and it is not used in any existing speech emotion recognition approaches.

In this paper, we present a new multimodal emotion recognition approach that improves the BERT model for emotion recognition by combining it with heterogeneous features based on language, audio, and visual modalities.

The existing protein fold recognition approaches focus on the first category to finding a robust and discriminative descriptor to represent each protein sequence as a compact feature vector, where different protein sequence is expected to be separated as much as possible in the fold space.

The majority of the existing machine learning-based protein fold recognition approaches strongly rely on handcrafted features, which depict the characteristics of different protein folds; however, effective feature extraction methods still represent the bottleneck for further performance improvement of protein fold recognition.

The experimental results show that the suggested model outperforms several existing recognition approaches that use the same data set.

ALPRNet treats LP and character equally, object detectors directly output bounding boxes of LPs and characters with corresponding labels, so they avoid the recurrent neural network (RNN) branches of optical character recognition (OCR) of the existing recognition approaches.