H13-321_V2.5 Exam Dumps - HCIP - AI EI Developer V2.5 Exam

In the HSV (Hue, Saturation, Value) color model:

Hrepresents hue (color type).

Srepresents saturation (color intensity or vividness).

Vrepresents brightness.

To enhance saturation in an image, adjustments are made to theS component. Increasing S increases the color vividness, making the image appear more vibrant, while reducing S moves colors toward grayscale. This approach is widely used in image enhancement tasks, especially in object recognition and segmentation, where vivid colors improve feature contrast.

Exact Extract from HCIP-AI EI Developer V2.5:

"In HSV color space, saturation (S) describes the vividness of colors. Increasing the S value enhances saturation, making colors more intense, while decreasing it makes them closer to gray."

The convolutional layer in CNNs is optimized for spatial feature extraction:

Local connectivity(A) reduces computation and memory usage.

Parameter sharing(B) reduces the number of learnable parameters and helps prevent overfitting.

Stride control(C) allows adjusting the output resolution and computational cost.

Sliding kernel operation(D) extracts local patterns without manual feature definition.

Exact Extract from HCIP-AI EI Developer V2.5:

"CNN convolutional layers leverage local connectivity, parameter sharing, and stride control to efficiently extract local features, reducing computational requirements compared to fully-connected layers."