Performance Of Computer Vision Systems Research Articles

Enhanced Edge Detection through Binary Particle Swarm Optimization and L0 Guided Filtering

Detecting edges holds significant importance in image processing, serving as a fundamental step in numerous computer vision applications. This paper presents an innovative method for performing edge detection by combining Binary Particle Swarm Optimization (BPSO) with L0 Guided Filtering. The proposed method aims to address the challenge of accurately detecting edges in noisy and complex images by leveraging the benefits of both BPSO and L0 guided filtering. The process begins with the initialization of the BPSO algorithm, where binary particles traverse the solution space to optimize parameters critical for edge detection. These optimized parameters are subsequently employed in the L0 guided filtering framework, a sophisticated edge preserving filter known for its ability to maintain fine details while effectively reducing noise. The synergy of BPSO and L0 guided filtering demonstrates improved adaptability to diverse image characteristics, enhancing the overall robustness of edge detection. The binary nature of BPSO allows for efficient exploration of the solution space, facilitating faster convergence to optimal parameters. Concurrently, the L0 guided filtering ensures edge preserving smoothing, contributing to the suppression of unwanted artifacts. Experimental evaluations on benchmark datasets showcase the effectiveness of the proposed method compared to traditional edge detection techniques. The results indicate superior edge localization and reduced sensitivity to noise, highlighting the potential of the BPSO Based Edge Detection under L0 Guided Filtering in real world applications. The presented approach offers a valuable contribution to the advancement of edge detection methodologies, demonstrating its potential for enhancing the performance of computer vision systems in various domains.

Automated Detection of Hillforts in Remote Sensing Imagery With Deep Multimodal Segmentation

ABSTRACTRecent advancements in remote sensing and artificial intelligence can potentially revolutionize the automated detection of archaeological sites. However, the challenging task of interpreting remote sensing imagery combined with the intricate shapes of archaeological sites can hinder the performance of computer vision systems. This work presents a computer vision system trained for efficient hillfort detection in remote sensing imagery. Equipped with an adapted multimodal semantic segmentation model, the system integrates LiDAR‐derived LRM images and aerial orthoimages for feature fusion, generating a binary mask pinpointing detected hillforts. Post‐processing includes margin and area filters to remove edge inferences and smaller anomalies. The resulting inferences are subjected to hard positive and negative mining, where expert archaeologists classify them to populate the training data with new samples for retraining the segmentation model. As the computer vision system is far more likely to encounter background images during its search, the training data are intentionally biased towards negative examples. This approach aims to reduce the number of false positives, typically seen when applying machine learning solutions to remote sensing imagery. Northwest Iberia experiments witnessed a drastic reduction in false positives, from 5678 to 40 after a single hard positive and negative mining iteration, yielding a 99.3% reduction, with a resulting F1 score of 66%. In England experiments, the system achieved a 59% F1 score when fine‐tuned and deployed countrywide. Its scalability to diverse archaeological sites is demonstrated by successfully detecting hillforts and other types of enclosures despite their typical complex and varied shapes. Future work will explore archaeological predictive modelling to identify regions with higher archaeological potential to focus the search, addressing processing time challenges.

DDR: A network of image deraining systems for dark environments

In the domain of computer vision, addressing the degradation of image quality under adverse weather conditions remains a significant challenge. To tackle the challenges of image enhancement and deraining in dark settings, we have integrated image enhancement and deraining technologies to develop the DDR (Dark Environment Deraining Network) system. This specialized network is designed to enhance and clarify images in low-light conditions compromised by raindrops. DDR employs a strategic divide-and-conquer approach and an apt network selection to discern patterns of raindrops and background elements within images. It is capable of mitigating noise and blurring induced by raindrops in dark settings, thus enhancing the visual fidelity of images. Through testing on real-world imagery and the Rain LOL dataset, this innovative network offers a robust solution for deraining tasks in dark conditions, inspiring advancements in the performance of computer vision systems under challenging weather scenarios. The research of DDR provides technical and theoretical support for improving image quality in dark environment.

Towards real world stereo image super-resolution via hybrid degradation model and discriminator for implied stereo image information

Real-world stereo image super-resolution has a significant influence on enhancing the performance of computer vision systems. Although existing methods for single-image super-resolution can be applied to enhance stereo images, these methods often introduce notable modifications to the inherent disparity, resulting in a loss in the consistency of disparity between the original and the enhanced stereo images. To overcome this limitation, this paper proposes a novel approach that integrates an implicit stereo information discriminator and a hybrid degradation model. This combination ensures effective enhancement while preserving disparity consistency. The proposed method bridges the gap between the complex degradations in real-world stereo domain and the simpler degradations in real-world single-image super-resolution domain. Our results demonstrate impressive performance on synthetic and real datasets, enhancing visual perception while maintaining disparity consistency.

Fuzzy-driven image enhancement via ABR-fractal-fractional differentiation

Low-light images often suffer from low sharpness and low contrast, hindering their visual quality and the performance of computer vision systems. To address this issue, we propose a novel image enhancement algorithm that combines local detail enhancement and global contrast enhancement. Our algorithm first transforms the crisp image into an intuitionistic fuzzy image. Next, we construct a global adaptive brightness transformation membership function to generate the first enhanced image. The second enhanced image is obtained using a newly proposed fractal-fractional differential operator. Finally, an image fusion technique is employed to effectively extract image features and integrate the locally and globally enhanced images. Our proposed technique effectively enhances the quality of low-light images while preserving their unique characteristics. Experimental results demonstrate that our method achieves comparable or even superior performance compared to state-of-the-art approaches, both qualitatively and quantitatively.

A 4-channelled hazy image input generation and deep learning-based single image dehazing

The images in foggy weather are often degraded which may affect the object detection process. Hence, in recent times, several schemes have been designed to eliminate the haze effect and enhance the performance of computer vision systems. Although these methods reduce the haze effect, they also produce over-saturation and over-degradation on most occasions. These problems occur due to inadequate utilization of the regional haze properties during the dehazing process. Therefore, a new dehazing model is proposed in this paper to address the issues faced by existing dehazing algorithms. The residual convolutional neural network (RCNN) with 14 layers is developed in the present work. In this model, features to indicate the extent of haze are extracted using the regional characteristics of a hazy image and are used as the fourth channel along with the three colour channels of a hazy image. The proposed RCNN is trained with 4-channel hazy images as input and their corresponding haze-free images as output. The results of our model are obtained with several images taken from three different datasets. Through the experimentation, quantitative and qualitative improvements are observed when compared to state-of-the-art dehazing methods.

Multiscale feature fusion deep network for single image dehazing with continuous memory mechanism

Haze scenes seriously degrade the performance of computer vision systems. Thus, the single image dehazing based on convolutional neural network (CNN), as an important direction of computer vision, has always attracted extraordinary attentions of researchers and made great progress on dehazing effects. However, there are still some non-trivial issues such as uneven dehazing and color distortion, which are not well addressed. To alleviate these issues, this work proposes a new multiscale feature fusion image dehazing network incorporating a contiguous memory mechanism (MFFDN-CM). Specifically, the pixel attention mechanism, continuous memory strategy and residual dense blocks are jointly integrated into the dehazing model with a prevalent encoder-decoder structure(U-Net). Firstly, our model obtains multiscale feature maps by subsampling operations, and further employs skip connections between the corresponding network layers to connect the feature maps between the encoder and the decoder for good feature fusion. Then, we introduce a continuous memory residual block to strengthen the information flows for feature reuse. Moreover, to leverage detail representation and accomplish adaptive dehazing according to the haze density, MFFDN-CM adopts a pixel attention module on the skip connections to combine the residual dense block module of the corresponding decoding layers. Finally, multiple residual blocks are exploited on the bottleneck in encoder-decoder structure to prevent network performance degradation due to vanishing gradients. Experimental results demonstrate the proposed model can achieve better dehazing performance than the state-of-the-art methods based on other deep neural networks.

AWDMC-Net: Classification of Adversarial Weather Degraded Multiclass scenes using a Convolution Neural Network

Computer vision systems in outdoor environments are strongly affected by different atmospheric/weather conditions. Therefore, understanding the actual behavior of outdoor scenes is necessary for effective removal and improvement of the overall performance of computer vision systems. Although the classification of atmospheric/weather conditions has been well explored, reporting on the same in multiclass problem using Convolutional Neural Networks (CNNs) has received very little attention. In response to address this disparity, we propose a new CNN architecture named the “Adversarial Weather Degraded Multi-class scenes Classification Network (AWDMC-Net)” for outdoor scene classification degraded by different atmospheric/weather conditions. The proposed network is based on adopting different combinations of skip connections in building blocks of CNN there after adaptively pruning the least important convolutional kernels from the network. For effective pruning, we proposed a new pruning criterion named “Entropy Guided Mean-l1 Norm” that can adaptively evaluate the importance of convolutional kernels by considering the filters and their corresponding output feature maps. The prediction performance of our proposed model was evaluated on our newly designed E-TUVD (Extended Tripura University Video Dataset) and on publicly available benchmark datasets. Our newly created video dataset, E-TUVD, consists of 147 video clips (approximately 793800 frames) that represent six atmospheric/weather conditions, namely, fog, dust, rain, haze, poor illumination, and clear day conditions. Our proposed model achieves an accuracy of 93.85%, a specificity of 93.79%, and a sensitivity of 94.18% on our dataset, which outperforms the prevailing standard CNN models and recent state-of-the-art methods for atmospheric/weather classification tasks. Furthermore, our network also reduces the time consumption for atmospheric/weather classification tasks, and therefore mostly meets the requirements of practical applications in real-world scenarios.

Spatial precision and recall indices to assess the performance of instance segmentation algorithms

Since it is essential for Computer Vision systems to reliably perform in safety-critical applications such as autonomous vehicles, there is a need to evaluate their robustness to naturally occurring image perturbations. More specifically, the performance of Computer Vision systems needs to be linked to the image quality, which hasn’t received much research attention so far. In fact, aberrations of a camera system are always spatially variable over the Field of View, which may influence the performance of Computer Vision systems dependent on the degree of local aberrations. Therefore, the goal is to evaluate the performance of Computer Vision systems under effects of defocus by taking into account the spatial domain. Large-scale Autonomous Driving datasets are degraded by a parameterized optical model to simulate driving scenes under physically realistic effects of defocus. Using standard evaluation metrics, the Spatial Recall Index (SRI) and the new Spatial Precision Index (SPI), the performance of Computer Vision systems on these degraded datasets are compared with the optical performance of the applied optical model. A correlation could be observed between the spatially varying optical performance and the spatial performance of Instance Segmentation systems.

SDNet: A Novel Separable Structure for Single Image Deraining

Rain streaks degrade visual quality of images and severely affect the performance of computer vision system. Recently, deep convolutional neural networks (DCNNs) have shown the potential in single image deraining. However, their complex structures with large-scale of parameters lead to strong requirement of large storage, which still limits the applications of deraining systems. In this paper, we propose a separable deraining network (SDNet) which provides a more flexible and simply baseline of deraining network by using a cascaded framework to achieve automatic trade-off between model performance and computational resources. Besides, a novel module called group-atrous spatial pyramid (G-ASP) and a network separation strategy (NSS) are proposed to extract more discriminative multi-scale information with fewer parameters and adapts our model to different levels rain-streaks for reasonable allocation of computational resources automatically. The experimental results on public datasets demonstrate that our SDNet achieves promising performances with much fewer parameters than other methods.

Measuring the Performance of Computer Vision Systems in Malaria Studies

Digital image processing-computer vision (DIP-CV) systems are used to automate malaria diagnosis through microscopy analysis of thin blood smears. Some variability is observed in the experimental design to evaluate the statistical measures of performance (SMP) of such systems. The objective of this work is assessing good practices when using SMP to evaluate DIP-CV systems for malaria diagnosis. A mathematical model was built to characterize diagnosis using DIP-CV systems and used to obtain curve families showing the relationships among various SMP of these systems, both using theoretical equations and computer simulation. Curve families showing (a) the relationships among the minimum number of positive erythrocytes (RBCs) to be observed, the per object (RBC) sensitivity and the probability to detect at least one positive, (b) per specimen sensitivity vs. total number of RBCs observed for a typical per object sensitivity and a range of parasite densities (c) per object positive predictive value vs. per object specificity for a typical per object sensitivity and various parasite densities. When determining the per specimen sensitivity, the parasite density <em>p</em> showed to have more influence on the number of RBCs that must be analyzed than the per object sensitivity. Measuring <em>p</em> accurately depends heavily upon the per object positive predictive value of the classifier. For low <em>p</em> values, this would require very high per object specificity and a high enough value of observed RBCs to measure this accurately.

Stacked dense networks for single-image snow removal

Single image snow removal is important since snowy images usually degrade the performance of computer vision systems. In this paper, we deduce a physics-based snow model and propose a novel snow removal method based on the snow model and deep neural networks. Our model decomposes a snowy image into a nonlinear combination of a snow-free image and dynamic snowflakes. Inspired by our model and DenseNet connectivity pattern, we design a novel Multi-scale Stacked Densely Connected Convolutional Network (MS-SDN) to simultaneously detect and remove snowflakes in an image. The MS-SDN is composed of a multi-scale convolutional sub-net for extracting feature maps and two stacked modified DenseNets for snowflakes detection and removal. The snowflake detection sub-net guides snow removal through forward transmission, and the snowflake removal sub-net adjusts snow detection through back transmission. In this way, snowflake detection and removal mutually improve the final results. For training and testing our method, we constructed a large-scale benchmark synthesis dataset which contains 3000 triplets of snowy images, snowflakes, and snow-free images. Specifically, the snow-free images are captured from snow scenes, and the snowy images are synthesized by using our deduced snow model. Our extensive quantitative and qualitative experimental results show that our MS-SDN performs better than several state-of-the-art methods, and the stacked structure is better than multi-branch structures in terms of snow removal.

Widening residual refine edge reserved neural network for semantic segmentation

Over the past several years, deep convolutional neural networks have pushed the performance of computer vision systems to soaring heights on semantic segmentation. In this paper, we put forward a novel practical convolutional neural network for effective semantic segmentation. The network is comprised of three modules. The module of widening residual convolutional neural network is firstly used to generate a full-size segmentation with localization of object boundaries. Feature maps generated by widening residual network are integrated by the module called as refine residual feature pyramid. The module of rolling guidance edge reserved layer is used for improving segmentation boundary. The innovations of this paper consist of these points. Widening residual skipped connections are utilized to preserve low-level features allowing accurate boundary localization. The refine residual feature pyramid is incorporated to achieve the observation of existence of objects at multiple scale. For modeling long-range context dependence and increasing accuracy near object boundary, rolling guidance edge reserved layer are implemented. The experimental results, on PASCAL VOC 2012 semantic segmentation dataset and Cityscapes dataset, demonstrate the proposed method can offer an effective way of producing pixel-wise image labeling.

Widening residual skipped network for semantic segmentation

Over the past two years deep convolutional neural networks have pushed the performance of computer vision systems to soaring heights on semantic segmentation. In this study, the authors present a novel semantic segmentation method of using a deep fully convolutional neural network to achieve image segmentation results with more precise boundary localisation. The above segmentation engine is trainable, and consists of an encoder network with widening residual skipped connections and a decoder network with a pixel-wise classification layer. Here the encoder network with widening residual skipped connections allows the combination of shallow layer features and deep layer semantic features, and the decoder network with classification layer maps the low-resolution encoder features to full resolution image with pixel-wise classification. The experimental results on PASCAL VOC 2012 semantic segmentation dataset and Cityscapes dataset show that the proposed method is effective and competitive.

A photographic negative imaging inspired method for low illumination night-time image enhancement

Images captured in low illumination conditions usually suffer from a poor visibility which has an important affect on the performance of computer vision systems. Thereby low illumination image enhancement is critical for image-related applications. In this paper, we proposed a photographic negative imaging inspired method for enhancing the images taken under low illumination environments. It consists of three basic components. First, the input night-time image is reversed to obtain its corresponding negative image (which is comparable with the latent image of a photographic film). Second, a rectification on the negative image is performed (Which is comparable with the chemical development in the process of film processing) by using an image dehazing method. This operation is inspired by the observation that the negative image looks like a hazed image and thereby it can be enhanced using a dehazing method. Third, the rectified negative image is reversed to obtain the final enhanced image (Which is comparable with the operation of fixing in the later stage of film processing.). Experiments over a large quantity of low contrast night-time images show that the proposed method is effective for enhancing low illumination images. Compared with six state-of-the-art image enhancement methods, the proposed method is superior to them in both enhancing image quality and decreasing time cost.

The design and implementation of a distributed image understanding system

Computer vision is concerned with extracting information about a scene by analyzing images of that scene. Performing any computer vision task requires an enormous amount of computation. Exploiting parallelism appears to be a promising way to improve the performance of computer vision systems. Past work in this area has focused on applying parallel processing techniques to image-operator level parallelism. In this article, we discuss the parallelism of computer vision in the control level and present a distributed image understanding system (DIUS). In DIUS, control-level parallelism is exploited by a dynamic scheduler. Furthermore, two levels of rules are used in the control mechanism. Meta-rules are concerned mainly with which strategy should be driven and the execution sequence of the system; control rules determine which task needs to be done next. A prototype system has been implemented within a parallel programming environment, Strand, which provides various virtual architectures mapping to either a shared-memory machine, Sequent, or to the Sun network.