Stack filters: Design algorithms and applications

Both the theory and application of stack filters are considered. The results obtained include new approaches to edge detection, new insights into the properties and proper uses of stack filters, and a new fast algorithm for training stack filters. The first approach to edge detection using stack filters is a generalization of median prefiltering: a stack filter is used to smooth an image before a standard gradient estimator is applied. These prefiltering schemes retain the robustness of the median prefilter, but allow resolution of finer detail. The second approach, called the Difference of Estimates (DoE) Approach, is a new formulation of a morphological scheme which has proven to be very sensitive to impulsive noise. In this approach, stack filters are applied to a noisy image to obtain local estimates of the dilated and eroded versions of the noise-free image. Thresholding the difference between these two estimates yields the edge map. We find, for example, that this approach yields results comparable to those obtained with the Canny operator for images with additive Gaussian noise, but works much better when the noise is impulsive. By defining the notion of a statistically symmetric image, an efficient design method called the Symmetric Difference of Estimates (SDoE) approach was proposed. In the SDoE approach, we can design the DoE operator with just one training run, and we can obtain unbiased estimates because of the symmetry constraint on the training data. The dual stack filters obtained under the SDoE approach are shown to be comparable. This property leads to a new scheme we call the Threshold Boolean Filter (TBF) approach. In the TBF approach, a Boolean function which may not be positive is used as a binary edge operator. This approach requires less training time but produces operators which are less robust than those produced by the DoE and SDoE approaches. Finally, to accelerate the training process for the design of stack filters, a new adaptive stack filtering algorithm is developed. The new algorithm retains the iterative nature of the present adaptive algorithms, but significantly reduces the number of iterations required in the training process. Also, due to the parallel nature of the new algorithm, the training process is much accelerated when it is implemented on the MasPar MP-1 parallel computer. The convergence property of the new algorithm is proved and the performance comparisons are made with the present adaptive algorithms

Moving Object Detection Using an Object Motion Reflection Model of Motion Vectors

Moving object detection task can be solved by the background subtraction algorithm if the camera is fixed. However, because the background moves, detecting moving objects in a moving car is a difficult problem. There were attempts to detect moving objects using LiDAR or stereo cameras, but when the car moved, the detection rate decreased. We propose a moving object detection algorithm using an object motion reflection model of motion vectors. The proposed method first obtains the disparity map by searching the corresponding region between stereo images. Then, we estimate road by applying v-disparity method to the disparity map. The optical flow is used to acquire the motion vectors of symmetric pixels between adjacent frames where the road has been removed. We designed a probability model of how much the local motion is reflected in the motion vector to determine if the object is moving. We have experimented with the proposed method on two datasets, and confirmed that the proposed method detects moving objects with higher accuracy than other methods

Personalized Text-to-Image Model Enhancement Strategies: SOD Preprocessing and CNN Local Feature Integration

Recent advancements in text-to-image models have been substantial, generating new images based on personalized datasets. However, even within a single category, such as furniture, where the structures vary and the patterns are not uniform, the ability of the generated images to preserve the detailed information of the input images remains unsatisfactory. This study introduces a novel method to enhance the quality of the results produced by text-image models. The method utilizes mask preprocessing with an image pyramid-based salient object detection model, incorporates visual information into input prompts using concept image embeddings and a CNN local feature extractor, and includes a filtering process based on similarity measures. When using this approach, we observed both visual and quantitative improvements in CLIP text alignment and DINO metrics, suggesting that the generated images more closely follow the text prompts and more accurately reflect the input image’s details. The significance of this research lies in addressing one of the prevailing challenges in the field of personalized image generation: enhancing the capability to consistently and accurately represent the detailed characteristics of input images in the output. This method enables more realistic visualizations through textual prompts enhanced with visual information, additional local features, and unnecessary area removal using a SOD mask; it can also be beneficial in fields that prioritize the accuracy of visual data