Do We Visually Experience Objects’ Occluded Parts?

A number of philosophers have held that we visually experience objects’ occluded parts, such as the out-of-view exterior of a voluminous, opaque object. That idea is supposed to be what best explains the fact that we see objects as whole or complete despite having only a part of them in view at any given moment. Yet, the claim doesn’t express a phenomenological datum and the reasons for thinking we do experience objects’ occluded parts, I argue, aren’t compelling. Additionally, I anticipate and reply to attempts to salvage the idea by appeal to perceptual expectation and amodal completion. Lastly, I address potential concerns that the only way to capture the phenomenal character of perceiving voluminous objects is to say experience outstrips what’s in view, providing a description of such experience without any implication of that idea.

An Efficient Way to Detect and Remove Shadows Based on Multiple Light Sources

A shadow is shaped when a light is blocked by an opaque object. They are sometimes unwanted as they may cause failure of image analysis and can also cause poor eminence of information. This paper first describes some important techniques that are used for shadow detection such as segmentation, Histogram Matching and some techniques that are used for shadow removal such as Morphological operations. The proposed methodology is to design a system based on three basic aims-the first goal is shadow detection of single and multiple images, second goal is to remove the shadow from the single and multiple images and third is to calculate the different parameter for measuring the quality of shadow removal method. Once the shadows are detected it becomes simple to detect a non shadow area which is estimated using morphological operators but sometimes when the shadow of any image is merged with the foreground object then the detection process becomes more complex

Scene distortion detection algorithm using multitemporal remote sensing images

Multitemporal remote sensing images of a particular territory might include accidental scene distortions. Scene distortion is a significant local brightness change caused by the scene overlap with some opaque object or a natural phenomenon coincident with the moment of image capture, for example, clouds and shadows. The fact that different images of the scene are obtained at different instants of time makes the appearance, location and shape of scene distortions accidental. In this article we propose an algorithm for detecting accidental scene distortions using a dataset of multitemporal remote sensing images. The algorithm applies superpixel segmentation and anomaly detection methods to get binary images of scene distortion location for each image in the dataset. The algorithm is adapted to handle images with different spectral and spatial sampling parameters, which makes it more multipurpose than the existing solutions. The algorithm's quality was assessed using model images with scene distortions for two remote sensing systems. The experiments showed that the proposed algorithm with the optimal settings can reach a detection accuracy of about 90% and a false detection error of about 10%.

A new tomography method in the presence of an opaque object

Разработан новый метод реконструкции изображения сечения объекта, содержащего непрозрачное включение. Для того чтобы оценить неизвестные данные в области тени, решается система линейных алгебраических уравнений, построенная на основе представления моментов проекций однородными полиномами. По результатам проведенного вычислительного эксперимента оказалось, что метод имеет преимущества перед альтернативными подходами. A new tomography method for a two-dimensional object containing an opaque inclusion is developed. For the estimation of unknown data in the opaque object's shadow, the system of linear algebraic equations derived from the representation of projections of moments by homogeneous polynomials is solved. The numerical results show that the method has a number of advantages over alternative approaches.

“Counterfactual” quantum protocols

The counterfactuality of recently proposed protocols is analyzed. A definition of “counterfactuality” is offered and it is argued that an interaction-free measurement (IFM) of the presence of an opaque object can be named “counterfactual”, while proposed “counterfactual” measurements of the absence of such objects are not counterfactual. The quantum key distribution protocols which rely only on measurements of the presence of the object are counterfactual, but quantum direct communication protocols are not. Therefore, the name “counterfactual” is not appropriate for recent “counterfactual” protocols which transfer quantum states by quantum direct communication.