Adaptive Color Model Research Articles

Underwater image enhancement utilizing adaptive color correction and model conversion for dehazing

Underwater imaging is typically affected by absorption and scattering, which can lead to color distortion and image blurring. However, no universal color constancy algorithm for correcting underwater images exists due to the wide variation in underwater scenes. Furthermore, methods based on learning to improve underwater image quality often suffer from a lack of ground truth, which can compromise the validity and authenticity of restored images. In this study, we propose an adaptive underwater image enhancement technique that corrects image color and reduces haze using a valid dataset with ground truth. Specifically, we first use hue channel statistics from underwater images to build a dataset of color-corrected images from different underwater scenes. We use this dataset to train an Unet-like network for adaptive color correction. After color correction, underwater images often exhibit characteristics similar to hazy terrestrial images. We refer to this phenomenon as “model conversion”. The hazy terrestrial images have corresponding ground truth. Therefore, we train a Transformer-like network with hazy terrestrial image datasets to remove haze in underwater images. Our method is more robust for different underwater scenes and elegantly solves the problem of lacking ground truth. A series of experiments demonstrate that our method achieves superior performance compared to state-of-the-art methods in terms of both visual quality and quantitative metrics.

A Novel Segmentation Method for Furnace Flame Using Adaptive Color Model and Hybrid-Coded HLO

In recent years, the combustion furnace has been widely applied in many different fields of industrial technology, and the accurate detection of combustion states can effectively help operators adjust combustion strategies to improve combustion utilization and ensure safe operation. However, the combustion states inside the industrial furnace change according to the production needs, which further challenges the optimal set of model parameters. To effectively segment the flame pixels, a novel segmentation method for furnace flame using adaptive color model and hybrid-coded human learning optimization (AHcHLO) is proposed. A new adaptive color model with mixed variables (NACMM) is designed for adapting to different combustion states, and the AHcHLO is developed to search for the optimal parameters of NACMM. Then, the best NACMM with optimal parameters is adopted to segment the combustion flame image more precisely and effectively. Finally, the experiment results show that the developed AHcHLO obtains the best-known overall results so far on benchmark functions and the proposed NACMM outperforms state-of-the-art flame segmentation approaches, providing a high detection accuracy and a low false detection rate.

Adaptive Skin Color Detection through Iterative Illuminant Color Estimation and Conversion for Preferred Skin Color Reproduction

A robust skin color detection method using adaptive skin color modeling on YES color space is proposed. The skin colors are classified into three skin regions according to the Y value, and each region is modeled as the skin color in ES plane. By introducing an iterative estimation method of the illuminant color, we effectively remove the pseudo-skin colors that have been erroneously detected in skin color using the standard deviation of E-histogram. We also improve the skin color detection rate using a post-processing expansion mask that can include highlight and shadow pixels near the skin region as the skin color region.

Identification method for furnace flame based on adaptive color model

A kiln and boiler combustion system is a complex nonlinear system. The method of an adaptive color model for furnace flame recognition is proposed to improve real-time flame detection and the recognition effect of combustion flames．First, a variable adaptive color model was designed by using the combustion characteristics of furnace flames. The expression of the threshold in the adaptive color model was deduced by the method of maximum classes square error. Based on this, the human-learning optimization algorithm was used to solve the optimal segmentation threshold. Then, fast and accurate identification of the combustion conditions of furnace flames was achieved. Simulation results are presented to verify the feasibility and effectiveness of the proposed results.

Skin color model adaptation under varying lighting conditions

Skin region detection is crucial for face recognition, hand tracking, and motion detection. In the detection process, a skin color model is usually required to confine the distribution of skin colors. However, skin color models are sensitive to lighting conditions. Skin segmentation under varying lighting conditions produces poor results. This article presents a skin detection procedure for human–computer interaction sessions under varying lighting conditions. The proposed method requests a skin sample from the user to estimate the color temperature of the light source. Then, the color temperature is used to correct the skin sample. At the subsequent step, the mean of the corrected skin sample is utilized to adapt the skin color model. Finally, the adapted skin color model is employed to segment skin regions in the video stream. Tests using the proposed method and some adaptive skin detection algorithms have been conducted. Statistical data show that the proposed method is superior to color constancy methods and the Gaussian mixture model in skin region segmentation. The proposed method improves the true positive rate by more than 13% in segmenting skin regions of a database. Its true positive rate is 20% better if real-life images are used as test data.

날씨·조명 판단 및 적응적 색상모델을 이용한 도로주행 영상에서의 이정표 검출

날씨·조명 판단 및 적응적 색상모델을 이용한 도로주행 영상에서의 이정표 검출

서베일런스 네트워크에서 적응적 색상 모델을 기초로 한 실시간 객체 추적 알고리즘

본 논문은 서베일런스 네트워크에서 영상의 색상 정보를 이용한 객체 추적 방법을 제안한다. 이 방법은 적응적인 색상 모델을 이용한 객체 검출을 수행한다. 객체 윤곽선 검출은 객체 인식과 같은 응용에서 중요한 역할을 수행한다. 실험 결과는 색상과 크기에서 객체의 다양한 변화가 있을 때에도 성공적인 객체 검출을 증명한다. 실시간으로 객체를 검출하는 응용 분야에서 대량의 영상 데이터를 전송할 때 색상 분포의 형태를 찾아내는 것이 가능하다. 객체의 특정 색상 정보는 입력 영상에서 동적으로 변화하는 색상에서 자주 수정되어진다. 그래서, 이 알고리즘은 해당 추적 영역 안에서 객체의 추적 영역 정보를 탐지하고 그 객체의 움직임만을 추적한다. 실험을 통해, 본 논문은 어떤 이상적인 상황하에서 제안하는 객체 추적 알고리즘이 다른 방법보다 더 강인한 면이 있다는 것을 보여준다. In this paper, we propose an object tracking method using the color information of the image in surveillance network. This method perform a object detection using of adaptive color model. Object contour detection plays an important role in application such as object recognition. Experimental results demonstrate successful object detection over a wide range of object's variation in color and scale. In applications to detect an object in real time, when transmitting a large amount of image data it is possible to find the mode of a color distribution. The specific color of an object is modified at dynamically changing color in image. So, this algorithm detects the tracking area information of object within relevant tracking area and only tracking the movement of that object.Through experiments, we show that proposed method is more robust than other methods under certain ideal situations.

Neuron-like algorithms of adaptive segmentation and semantic description of images in the “adult image” recognition problem

We describe the possibility of using the methods of color segmentation and semantic description of images in the "adult image" recognition problem. A method for the construction of an adaptive color model of human skin, which uses information about the faces found in the image, is proposed. The possibility of using the semantic features obtained by face attribute detectors for a more detailed classification of such images is also considered.

A Web Replay Method for Highlights in Soccer Videos

A web replay method is proposed in this paper for highlights in soccer videos. Firstly, automatically detection and fine-grained semantic annotation are introduced for soccer highlights. Secondly, an adaptive grass color model and several heuristic rules are used to improve the performance of the method. Finally, a database for highlights is established for web service. Experimental results show that the proposed method has excellent analyzing speed, accuracy and practicability.

Data association and occlusion handling for vision-based people tracking by mobile robots

This paper presents an approach for tracking multiple persons on a mobile robot with a combination of colour and thermal vision sensors, using several new techniques. First, an adaptive colour model is incorporated into the measurement model of the tracker. Second, a new approach for detecting occlusions is introduced, using a machine learning classifier for pairwise comparison of persons (classifying which one is in front of the other). Third, explicit occlusion handling is incorporated into the tracker. The paper presents a comprehensive, quantitative evaluation of the whole system and its different components using several real world data sets.

Bilateral learning for color-based tracking

This paper addresses the issue of color model adaptation and color-based object tracking in a dynamic scene. Under different environmental conditions such as illumination changes, a static color model is inadequate for the purpose of color-based object detection and tracking. Color model adaptation is required and this has to be integrated into the tracking procedure within the spatial domain. To track a target in both the color and spatial domains, a Bilateral Learning (BL) approach is proposed in this paper. Formulated as an unsupervised learning problem, the adaptations of the color and spatial models are fitted into an EM framework by updating in the color and image spaces alternately. This results in the adaptation of the color model and the localization of the target along the image sequence. Experimental results show the effectiveness and efficacy of the proposed approach for color model adaptation and object tracking under illumination changes and environmental noise in real time.

Robust face tracking by integration of two separate trackers: Skin color and facial shape

This paper proposes a robust face tracking method based on the condensation algorithm that uses skin color and facial shape as observation measures. Two trackers are used for robust tracking: one tracks the skin color regions and the other tracks the facial shape regions. The two trackers are coupled using an importance sampling technique, where the skin color density obtained from the skin color tracker is used as the importance function to generate samples for the shape tracker. The samples of the skin color tracker within the chosen shape region are updated with higher weights. Also, an adaptive color model is used to avoid the effect of illumination change in the skin color tracker. The proposed face tracker performs more robustly than either the skin-color-based tracker or the facial shape-based tracker, given the presence of background clutter and/or illumination changes.

얼굴의 색상과 모양정보를 이용한 조명 변화에 강인한 얼굴 추적 시스템 구현

얼굴 인식 시스템과 표정인식 시스템과 같은 고차원 처리를 수행하는 시스템의 전처리 과정으로써 이미지 영역 내에서 얼굴을 추적하는 것은 중요한 과정이다. 본 논문에서는 피부색과 얼굴의 형태 정보를 단서로 하는 CONDENSATION 알고리즘을 사용하여 얼굴의 위치를 추적하였다. 컬러의 가중치와 형태의 가중치를 결합시키는데 어려움이 있으므로 각각을 단서로 사용하는 두 개의 추적기를 가진 시스템을 제안한다. 제안된 시스템은 복잡한 배경, 피부색의 물체가 이미지 내에 존재하는 경우, 다른 얼굴이 이미지 내에 존재하는 경우 모두에 대해서 훌륭한 성능을 보여 주었다. In this paper, we propose a robust face tracking algorithm. It is based on Condensation algorithm [7] and uses skin color and facial shape as the observation measure. It is hard to integrate color weight and shape weight. So we propose the method that has two separate trackers which uses skin color and facial shape as the observation measure respectively. One tracker tracks skin colored region and the other tracks facial shape. We used importance sampling technique to limit sampling region of two trackers. For skin-colored region tracker, we propose an adaptive color model to avoid the effect of illumination change. The proposed face tracker performs robustly in clutter background and in the illumination changes.

Nonstationary color tracking for vision-based human-computer interaction

Skin color offers a strong cue for efficient localization and tracking of human body parts in video sequences for vision-based human-computer interaction. Color-based target localization could be achieved by analyzing segmented skin color regions. However, one of the challenges of color-based target tracking is that color distributions would change in different lighting conditions such that fixed color models would be inadequate to capture nonstationary color distributions over time. Meanwhile, using a fixed skin color model trained by the data of a specific person would probably not work well for other people. Although some work has been done on adaptive color models, this problem still needs further studies. We present our investigation of color-based image segmentation and nonstationary color-based target tracking, by studying two different representations for color distributions. We propose the structure adaptive self-organizing map (SASOM) neural network that serves as a new color model. Our experiments show that such a representation is powerful for efficient image segmentation. Then, we formulate the nonstationary color tracking problem as a model transduction problem, the solution of which offers a way to adapt and transduce color classifiers in nonstationary color distributions. To fulfill model transduction, we propose two algorithms, the SASOM transduction and the discriminant expectation-maximization (EM), based on the SASOM color model and the Gaussian mixture color model, respectively. Our extensive experiments on the task of real-time face/hand localization show that these two algorithms can successfully handle some difficulties in nonstationary color tracking. We also implemented a real-time face/hand localization system based on such algorithms for vision-based human-computer interaction.

Adaptive skin color modeling using the skin locus for selecting training pixels

Techniques for color-based tracking of faces or hands often assume a static skin model yet skin color, as measured by a camera, can change when lighting changes. Therefore, for robust skin pixel detection, an adaptive skin color model must be employed. We demonstrate a chromaticity-based constraint to select training pixels in a scene for updating a dynamic skin color model under changing illumination conditions. The method makes use of the ‘skin locus’ of a camera, that is, the area in chromaticity space where skin chromaticity under various lighting and camera calibration conditions is observed. Skin color models derived from the technique are compared with that derived by a common spatial constraint and is shown to be more consistent with manually extracted ground truth skin model per frame even as localization errors increase. The technique is applied to color-based face tracking in indoor and outdoor videos and is shown to succeed more often than other color model adaptation techniques.