Auto Topic: pixels

auto_pixels | topic

Coverage Score

1

Mentioned Chunks

50

Mentioned Docs

1

Required Dimensions

definitionpros_cons

Covered Dimensions

definitionpros_cons

Keywords

pixels

Relations

Source	Type	Target	W
Auto Topic: pixels	CO_OCCURS	Auto Topic: window	7
Auto Topic: convolutional	CO_OCCURS	Auto Topic: pixels	7
Auto Topic: convolution	CO_OCCURS	Auto Topic: pixels	7
Auto Topic: pixels	CO_OCCURS	Propositional Logic	6
Auto Topic: pixels	CO_OCCURS	Constraint Satisfaction Problem	6
Auto Topic: pixels	CO_OCCURS	Auto Topic: scene	6
Auto Topic: dimension	CO_OCCURS	Auto Topic: pixels	6
Auto Topic: kernel	CO_OCCURS	Auto Topic: pixels	6
Auto Topic: detector	CO_OCCURS	Auto Topic: pixels	6
Auto Topic: pixels	CO_OCCURS	Auto Topic: spatial	5
Auto Topic: cnn	CO_OCCURS	Auto Topic: pixels	5
Auto Topic: pixels	CO_OCCURS	Logical Agents	4
Auto Topic: pixels	CO_OCCURS	State-Space Search	4
Auto Topic: pixels	CO_OCCURS	Auto Topic: stride	4
Auto Topic: pixels	CO_OCCURS	Auto Topic: windows	4
Auto Topic: pixels	CO_OCCURS	Task Environment	3
Auto Topic: activation	CO_OCCURS	Auto Topic: pixels	3
Auto Topic: boundary	CO_OCCURS	Auto Topic: pixels	3

Evidence Chunks

Source	Confidence	Mentions	Snippet
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf	0.67	7	... or [+1, −1, +1], which detects a darker point in the 1D image. (The 2D version might detect a darker line.) Notice that in this example the pixels on which the kernels are centered are separated by a distance of 2 pixels; we say the kernel is applied with a stride s =2. Notice th ...
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf	0.65	6	y pixels formed by thresholding Pb(x,y,θ ) are not guaranteed to form closed curves, so this approach doesn’t deliver regions, and (2) the decision making exploits only local context, and does not use global consistency constraints. The alternative approach is based on trying to ...
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf	0.61	4	... mentioned in Section 22.2.1 that an image cannot be thought of as a simple vector of in- put pixel values, primarily because adjacency of pixels really matters. If we were to construct a network with fully connected layers and an image as input, we would get the same result whet ...
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf	0.61	4	the image ﬁrst, the spurious peaks are diminished, as we see in Figure 27.7 (bottom). Smoothing involves using surrounding pixels to suppress noise. We will predict the “true” value of our pixel as a weighted sum of nearby pixels, with more weight for the closest pixels. A natura ...
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf	0.61	4	... is a local maximum along the direction of the gradient, and the gradient magnitude is above a suitable threshold. Once we have marked edge pixels by this algorithm, the next stage is to link those pixels that belong to the same edge curves. This can be done by assuming that any ...
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf	0.59	3	... eceptive ﬁeld grows linearly with m, so the area grows quadratically.) When the stride is larger than 1, each pixel in layer m represents s pixels in layer m − 1; therefore, the receptive ﬁeld grows asO(lsm)—that is, exponentially with depth. The same effect occurs with pooling l ...
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf	0.59	3	... =s, the effect is to coarsen the resolution of the image—to downsample it—by a factor of s. An object Downsampling that occupied, say, 10s pixels would now occupy only 10 pixels after pooling. The same 814 Chapter 22 Deep Learning learned classiﬁer that would be able to recogniz ...
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf	0.59	3	... evel” operation. The other operations require handling a larger area of the image. For example, a texture description applies to a pool of pixels—to say “stripey,” you need to see some stripes. Optical ﬂow represents where pixels move to from one image in a sequence to the next, ...
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf	0.59	3	he next stage is to link those pixels that belong to the same edge curves. This can be done by assuming that any two neighboring pixels that are both edge pixels with consistent orientations belong to the same edge curve. Edge detection isn’t perfect. Figure 27.8(a) shows an imag ...
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf	0.59	3	... e-minded technique is based on the fact that image patches around corresponding points have similar intensity patterns. Consider a block of pixels centered at pixel p, (x0,y0), at time t. This block of pixels is to be compared with pixel blocks centered at various candidate pixel ...
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf	0.59	3	... aptured by the directions of the arrows. (Images courtesy of Thomas Brox.) Section 27.3 Simple Image Features 1001 Here, (x,y) ranges over pixels in the block centered at (x0,y0). We ﬁnd the (Dx,Dy) that minimizes the SSD. The optical ﬂow at (x0,y0) is then (vx,vy) = (Dx/Dt,Dy/Dt ...
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf	0.59	3	... verall class. When you are classifying images of scenes, every pixel could be helpful. But when you are classifying images of objects, some pixels aren’t part of the object, and so might be a 1006 Chapter 27 Computer Vision distraction. For example, if a cat is lying on a dog bed ...
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf	0.59	3	... (because moving by one pixel is not likely to change the classiﬁcation); a good choice is astride (the offset between center points) of 16 pixels. For each center point we consider several possible boxes, called anchor boxes. Faster RCNN uses nine boxes: small, medium, and large ...
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf	0.57	2	... , the values can be mapped onto a log scale. Images do not quite ﬁt into the category of factored data; although an RGB image of size X ×Y pixels can be thought of as 3 XY integer-valued attributes (typically with values in the range {0,..., 255}), this would ignore the fact that ...
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf	0.57	2	... yer. A pattern of weights that is replicated across multiple local regions is called a kernel and the process of applying the kernel to the pixels of the image (or to spatially Kernel organized units in a subsequent layer) is called convolution.4 Convolution Kernels and convoluti ...
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf	0.57	2	... ect that neuron’s activation. In a CNN, the receptive ﬁeld of a unit in the ﬁrst hidden layer is small—just the size of the kernel, i.e., l pixels. In the deeper layers of the network, it can be much larger. Figure 22.5 illustrates this for a unit in the second hidden layer, whos ...
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf	0.57	2	... ge to an input can cause a large change in the output. For example, it may Section 22.5 Generalization 821 be possible to alter just a few pixels in an image of a dog and cause the network to classify the dog as an ostrich or a school bus—even though the altered image still looks ...
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf	0.57	2	... n ﬁlm cameras the image plane is coated with silver halides. In digital cameras, the image plane is subdivided into a grid of a few million pixels. Pixels We refer to the whole image plane as a sensor, but each pixel is an individual tiny Sensor sensor—usually a charge-coupled de ...