A system and method for providing a weather effect in an image includes selecting at least one weather texture image indicating weather, and providing a weather effect in the image by overlapping the selected weather texture image on the image.

According to an embodiment, a device for detecting fog on a road comprises an imaging device installed to capture a two-way road and capturing a fog on the two-way road, a network configuring device provided under the imaging device and transmitting an image captured by the imaging device, a fog monitoring device receiving the image from the network configuring device, analyzing the image to thereby detect the fog, and outputting an alert per predetermined crisis level, and a display device displaying the alert output from the fog monitoring device and transmitting the alert via a wired or wireless network.

Examples relate to a method, an apparatus and a computer program for detecting a presence of airborne particles. A reference measurement of an environment of a depth image sensor module is obtained. The reference measurement is based on a measurement of modulated light in a first time interval. The modulated light is reflected by features of the environment of the depth image sensor module. A subsequent measurement of modulated light is obtained in a second time interval. The presence of the airborne particles is detected based on the subsequent measurement of the modulated light, by using the reference measurement performed in the first time interval to disregard all or part of the features of the environment of the depth image sensor module. A signal indicative of one or more properties of the detected airborne particles is generated based on the detected presence of the airborne particles.

To provide a cloud observation device capable of reducing calculation cost and predicting sunshine probability by a simple method. A cloud observation device includes an image acquisition module which acquires an image in which a camera photographs the sky, a cloud extraction module which extracts clouds in the image, a sun position determination module which determines a sun position in the image, a sunshine probability calculation area setting module which sets a sunshine probability calculation area having the sun position as a base point in the image, and a sunshine probability calculation module which calculates a sunshine probability after a predetermined time has elapsed based on the sunshine probability calculation area and the extracted clouds.

For enabling safer operation of freight trains, a fixed state inspection apparatus includes: a frame image group acquisition unit acquiring frame image group information being information including a plurality of frame images acquired by continuously capturing images of a freight train along a traveling direction, at least one of the frame images including a fixing mechanism that is provided in association with a container in the freight train and can switch between a fixed state and a released state between members; a detection unit detecting the fixing mechanism in an unfixed state different from the fixed state in the freight train, based on the frame image group information; and an output unit generating and outputting detection position information being information allowing determination of a position of the detected fixing mechanism in the freight train, based on a detection frame image being a frame image including the detected fixing mechanism.

Disclosed is a river flow velocity measurement device using optical flow image processing, including: an image photographing unit configured to acquire consecutive images of a flow velocity measurement site of a river; an image conversion analysis unit configured to dynamically extract frames of the consecutive images in order to normalize image data of the image photographing unit, image-convert the extracted frames, and perform homography calculation; an analysis region extracting unit configured to extract an analysis region of an analysis point; a pixel flow velocity calculating unit configured to calculate a pixel flow velocity using an image in the analysis region of the analysis point extracted by the analysis region extracting unit; and an actual flow velocity calculating unit configured to convert the pixel flow velocity calculated by the pixel flow velocity calculating unit into an actual flow velocity.

A system for detecting clouds and cloud shadows is described. In one approach, clouds and cloud shadows within a remote sensing image are detected through a three step process. In the first stage a high-precision low-recall classifier is used to identify cloud seed pixels within the image. In the second stage, a low-precision high-recall classifier is used to identify potential cloud pixels within the image. Additionally, in the second stage, the cloud seed pixels are grown into the potential cloud pixels to identify clusters of pixels which have a high likelihood of representing clouds. In the third stage, a geometric technique is used to determine pixels which likely represent shadows cast by the clouds identified in the second stage. The clouds identified in the second stage and the shadows identified in the third stage are then exported as a cloud mask and shadow mask of the remote sensing image.

According to one aspect, scene classification may be provided. An image capture device may capture a series of image frames of an environment from a moving vehicle. A temporal classifier may classify image frames with temporal predictions and generate a series of image frames associated with respective temporal predictions based on a scene classification model. The temporal classifier may perform classification of image frames based on a convolutional neural network (CNN), a long short-term memory (LSTM) network, and a fully connected layer. The scene classifier may classify image frames based on a CNN, global average pooling, and a fully connected layer and generate an associated scene prediction based on the scene classification model and respective temporal predictions. A controller of a vehicle may activate or deactivate vehicle sensors or vehicle systems of the vehicle based on the scene prediction.

Methods, systems, and apparatus for removing precipitation from video are disclosed. A method includes generating, from a first set of images of a scene from a camera, a segmented background image model of the scene; obtaining a second set of images from the camera; identifying, in an image of the second set of images, a plurality of edges, determining that a first edge of the plurality of edges satisfies criteria for representing precipitation based at least in part on determining that the first edge (i) does not correspond to the background image model of the scene and (ii) extends into two or more contiguous segments of the scene; in response, classifying each of the contiguous segments as a precipitation segment; generating pixel data for each of the precipitation segments; and applying the pixel data to each precipitation segment in the image.

Disclosed is an image descattering method based on iterative optimization of an atmospheric transmission matrix, including steps: S1, constructing a descattering model based on the atmospheric transmission matrix; S2, estimating a forward scattering coefficient q corresponding to a foggy day image B; S3, based on a depth map T of the foggy day image B and the forward scattering coefficient q obtained in the step S2, estimating an initial atmospheric transmission matrix A; and S4, substituting the estimated initial atmospheric transmission matrix A into the descattering model in the step S1, and performing iterative update under a constraint condition, until a value of the descattering model satisfies a convergence condition, to obtain an optimal atmospheric transmission matrix A* and an optimal descattered image X*. The image descattering method based on the iterative optimization of the atmospheric transmission matrix provided by the present application is based on the atmospheric transmission matrix.