Sketch2Photo: Synthesizing photo-realistic images from sketches via global contexts

Liu, Heng; Yao, Xu; Chen, Feng

doi:10.1016/j.engappai.2022.105608

Cited by 68 publications

(25 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the context of signal processing for accelerometer data, skewness is a crucial statistical measure that captures the asymmetry [68][69][70] of the signal distribution. To compute the skewness of an accelerometer signal s(n), where n represents the discrete time index, we first calculate the mean (µ) and standard deviation (σ) of the signal.…”

Section: Skewnessmentioning

confidence: 99%

Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization

Almujally,

Khan,

Al Mudawi

et al. 2024

Sensors

View full text Add to dashboard Cite

The domain of human locomotion identification through smartphone sensors is witnessing rapid expansion within the realm of research. This domain boasts significant potential across various sectors, including healthcare, sports, security systems, home automation, and real-time location tracking. Despite the considerable volume of existing research, the greater portion of it has primarily concentrated on locomotion activities. Comparatively less emphasis has been placed on the recognition of human localization patterns. In the current study, we introduce a system by facilitating the recognition of both human physical and location-based patterns. This system utilizes the capabilities of smartphone sensors to achieve its objectives. Our goal is to develop a system that can accurately identify different human physical and localization activities, such as walking, running, jumping, indoor, and outdoor activities. To achieve this, we perform preprocessing on the raw sensor data using a Butterworth filter for inertial sensors and a Median Filter for Global Positioning System (GPS) and then applying Hamming windowing techniques to segment the filtered data. We then extract features from the raw inertial and GPS sensors and select relevant features using the variance threshold feature selection method. The extrasensory dataset exhibits an imbalanced number of samples for certain activities. To address this issue, the permutation-based data augmentation technique is employed. The augmented features are optimized using the Yeo–Johnson power transformation algorithm before being sent to a multi-layer perceptron for classification. We evaluate our system using the K-fold cross-validation technique. The datasets used in this study are the Extrasensory and Sussex Huawei Locomotion (SHL), which contain both physical and localization activities. Our experiments demonstrate that our system achieves high accuracy with 96% and 94% over Extrasensory and SHL in physical activities and 94% and 91% over Extrasensory and SHL in the location-based activities, outperforming previous state-of-the-art methods in recognizing both types of activities.

show abstract

Section: Skewnessmentioning

confidence: 99%

Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization

Almujally,

Khan,

Al Mudawi

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…In this research, a new database is also used for training the convolutional model in a similar manner. In general, deep learning techniques due to their high performance in image processing have been used in various applications such as image classification 16 , image reconstruction 17 or synthesizing photo-realistic images 18 ; and the application areas of these models are increasing 19 .…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing support vector machine (SVM) by social spider optimization (SSO) for edge detection in colored images

Wang

2024

Sci Rep

View full text Add to dashboard Cite

Edge detection in images is a vital application of image processing in fields such as object detection and identification of lesion regions in medical images. This problem is more complex in the domain of color images due to the combination of color layer information and the need to achieve a unified edge boundary across these layers, which increases the complexity of the problem. In this paper, a simple and effective method for edge detection in color images is proposed using a combination of support vector machine (SVM) and the social spider optimization (SSO) algorithm. In the proposed method, the input color image is first converted to a grayscale image, and an initial estimation of the image edges is performed based on it. To this end, the proposed method utilizes an SVM with a Radial Basis Function (RBF) kernel, in which the model's hyperparameters are tuned using the SSO algorithm. After the formation of initial image edges, the resulting edges are compared with pairwise combinations of color layers, and an attempt is made to improve the edge localization using the SSO algorithm. In this step, the optimization algorithm's task is to refine the image edges in a way that maximizes the compatibility with pairwise combinations of color layers. This process leads to the formation of prominent image edges and reduces the adverse effects of noise on the final result. The performance of the proposed method in edge detection of various color images has been evaluated and compared with similar previous strategies. According to the obtained results, the proposed method can successfully identify image edges more accurately, as the edges identified by the proposed method have an average accuracy of 93.11% for the BSDS500 database, which is an increase of at least 0.74% compared to other methods.

show abstract

“…Similarly, a 3-D Contextual deep CNN (3D-FCN) [ 35 ] was suggested to optimize the exploration of local contextual interactions among neighboring individual pixel vectors. When we talk about applications of computer vision, then there are many research works done so far like on wheat classification [ 46 ], brightness correction [ 47 ], pattern analysis [ 48 , 49 ], and photo-synthesis [ 50 ]. The transformer learning models also perform well for target object detection [ 51 – 53 ].…”

Section: Related Workmentioning

confidence: 99%

Attention 3D central difference convolutional dense network for hyperspectral image classification

Ashraf,

Alharthi,

Chen

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

Hyperspectral Images (HSI) classification is a challenging task due to a large number of spatial-spectral bands of images with high inter-similarity, extra variability classes, and complex region relationships, including overlapping and nested regions. Classification becomes a complex problem in remote sensing images like HSIs. Convolutional Neural Networks (CNNs) have gained popularity in addressing this challenge by focusing on HSI data classification. However, the performance of 2D-CNN methods heavily relies on spatial information, while 3D-CNN methods offer an alternative approach by considering both spectral and spatial information. Nonetheless, the computational complexity of 3D-CNN methods increases significantly due to the large capacity size and spectral dimensions. These methods also face difficulties in manipulating information from local intrinsic detailed patterns of feature maps and low-rank frequency feature tuning. To overcome these challenges and improve HSI classification performance, we propose an innovative approach called the Attention 3D Central Difference Convolutional Dense Network (3D-CDC Attention DenseNet). Our 3D-CDC method leverages the manipulation of local intrinsic detailed patterns in the spatial-spectral features maps, utilizing pixel-wise concatenation and spatial attention mechanism within a dense strategy to incorporate low-rank frequency features and guide the feature tuning. Experimental results on benchmark datasets such as Pavia University, Houston 2018, and Indian Pines demonstrate the superiority of our method compared to other HSI classification methods, including state-of-the-art techniques. The proposed method achieved 97.93% overall accuracy on the Houston-2018, 99.89% on Pavia University, and 99.38% on the Indian Pines dataset with the 25 × 25 window size.

show abstract

Sketch2Photo: Synthesizing photo-realistic images from sketches via global contexts

Cited by 68 publications

References 37 publications

Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization

Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization

Optimizing support vector machine (SVM) by social spider optimization (SSO) for edge detection in colored images

Attention 3D central difference convolutional dense network for hyperspectral image classification

Contact Info

Product

Resources

About