Shadow detection is a fundamental and challenging task, since it requires an understanding of global image semantics and there are various backgrounds around shadows. This paper presents a novel network for shadow detection by analyzing image context in a direction-aware manner. To achieve this, we first formulate the direction-aware attention mechanism in a spatial recurrent neural network (RNN) by introducing attention weights when aggregating spatial context features in the RNN. By learning these weights through training, we can recover direction-aware spatial context (DSC) for detecting shadows. This design is developed into the DSC module and embedded in a CNN to learn DSC features at different levels. Moreover, a weighted cross entropy loss is designed to make the training more effective. We employ two common shadow detection benchmark datasets and perform various experiments to evaluate our network. Experimental results show that our network outperforms state-of-the-art methods and achieves 97 accuracy and 38 reduction on balance error rate.
Traditionally, single-image shadow detection is done by exploiting physical models of illumination and color @cite_27 @cite_21 @cite_0 . This approach, however, tends to produce satisfactory results only for wide dynamic range images @cite_4 @cite_5 . Another approach learns shadow properties using hand-crafted features based on annotated shadow images. It first describes image regions by feature descriptors and then classifies the regions into shadow and non-shadow regions. Features like color @cite_4 @cite_2 @cite_13 , texture @cite_17 @cite_2 @cite_13 , edge @cite_4 @cite_17 @cite_22 and T-junction @cite_4 are commonly used for shadow detection followed by classifiers like decision tree @cite_4 @cite_17 and SVM @cite_2 @cite_22 @cite_13 . However, since hand-crafted features have limited capability in describing shadows, this approach often fails for complex cases.
@cite_4 @cite_22 @cite_21 @cite_0 @cite_27 @cite_2 @cite_5 @cite_13 @cite_17
1967913888 2147558395 2073839959 2547609471
In this paper, we address the problem of shadow detection and removal from single images of natural scenes. Differently from traditional methods that explore pixel or edge information, we employ a region-based approach. In addition to considering individual regions separately, we predict relative illumination conditions between segmented regions from their appearances and perform pairwise classification based on such information. Classification results are used to build a graph of segments, and graph-cut is used to solve the labeling of shadow and nonshadow regions. Detection results are later refined by image matting, and the shadow-free image is recovered by relighting each pixel based on our lighting model. We evaluate our method on the shadow detection dataset in . In addition, we created a new dataset with shadow-free ground truth images, which provides a quantitative basis for evaluating shadow removal. We study the effectiveness of features for both unary and pairwise classification. In this paper, we address the problem of shadow detection and removal from single images of natural scenes. Different from traditional methods that explore pixel or edge information, we employ a region based approach. In addition to considering individual regions separately, we predict relative illumination conditions between segmented regions from their appearances and perform pairwise classification based on such information. Classification results are used to build a graph of segments, and graph-cut is used to solve the labeling of shadow and non-shadow regions. Detection results are later refined by image matting, and the shadow free image is recovered by relighting each pixel based on our lighting model. We evaluate our method on the shadow detection dataset in [19]. In addition, we created a new dataset with shadow-free ground truth images, which provides a quantitative basis for evaluating shadow removal. Despite decades of study, robust shadow detection remains difficult, especially within a single color image. We describe a new approach to detect shadow boundaries in images of outdoor scenes lit only by the sun and sky. The method first extracts visual features of candidate edges that are motivated by physical models of illumination and occluders. We feed these features into a Support Vector Machine (SVM) that was trained to discriminate between most-likely shadow-edge candidates and less-likely ones. Finally, we connect edges to help reject non-shadow edge candidates, and to encourage closed, connected shadow boundaries. On benchmark shadow-edge data sets from and , our method showed substantial improvements when compared to other recent shadow-detection methods based on statistical learning. Detecting cars from high-resolution remote sensing images is vulnerable to the effect of shadows in the image, as a result, cars in shadow areas are hard to be detected due to their weak visual features. In order to solve this problem, a vehicle detection method which takes the shaded areas into account is proposed in this letter. Firstly, we extract shadows in road areas using a shadow detection algorithm which is based on color features, and then we enhance the shaded areas by histogram equalization to improve cars' visual characteristics; Secondly, vehicle detection models M 1 ,M 2 are trained in shaded and non-shaded regions respectively using HOG features combined with SVM classification method. Finally, we use M 1 to extract cars in shadows and the ones which are not in shadows are detected by M 2 , then the final result is obtained through the combination of the two outcomes. Experiments using multiple sets of test images show that: in contrast with traditional car counting method, the proposed one has the ability to improve the performance of vehicle detection, the probability of wrong detection is decreased as well.