The upper and lower compensated convex transforms are `tight' one-sided approximations for a given function. We apply these transforms to the extraction of fine geometric singularities from general semiconvex/semiconcave functions and DC-functions in $\mathbb{R}^n$ (difference of convex functions). Well-known geometric examples of (locally) semiconcave functions include the Euclidean distance function and the Euclidean squared-distance function. For a locally semiconvex function $f$ with general modulus, we show that `locally' a point is singular (a non-differentiable point) if and only if it is a scale $1$-valley point, hence by using our method we can extract all fine singular points from a given semiconvex function. More precisely, if $f$ is a semiconvex function with general modulus and $x$ is a singular point, then locally the limit of the scaled valley transform exists at every point $x$ and can be calculated as $ \lim_{\lambda\to+\infty}\lambda V_\lambda (f)(x)=r_x^2/4$, where $r_x$ is the radius of the minimal bounding sphere of the (Frechet) subdifferential $\partial_- f(x)$ of the locally semiconvex $f$ and $V_\lambda (f)(x)$ is the valley transform at $x$. Thus the limit function $ \mathcal{V}_\infty(f)(x):=\lim_{\lambda\to+\infty}\lambda V_\lambda (f)(x)=r_x^2/4$ provides a `scale $1$-valley landscape function' of the singular set for a locally semiconvex function $f$. At the same time, the limit also provides an asymptotic expansion of the upper transform $C^u_\lambda(f)(x)$ when $\lambda$ approaches $+\infty$. For a locally semiconvex function $f$ with linear modulus we show further that the limit of the gradient of the upper compensated convex transform $ \lim_{\lambda\to+\infty}\nabla C^u_\lambda(f)(x)$ exists and equals the centre of the minimal bounding sphere of $\partial_- f(x)$. We also show that for a DC-function $f=g-h$, the scale $1$-edge transform, when $\lambda\to+\infty$, satisfies $ \liminf_{\lambda\to+\infty}\lambda E_\lambda (f)(x)\geq (r_{g,x}-r_{h,x})^2/4$, where $r_{g,x}$ and $r_{h,x}$ are the radii of the minimal bounding spheres of the subdifferentials $\partial_- g$ and $\partial_- h$ of the two convex functions $g$ and $h$ at $x$, respectively.
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